{
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  "sourcesContent": ["import{jsx as e,jsxs as t}from\"react/jsx-runtime\";import{Link as n}from\"framer\";import*as i from\"react\";export const richText=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Explore how Stereo-seq unveils cellular senescence and aging with high-resolution spatial transcriptomics, offering new insights into age-related tissue changes and potential therapeutic targets.\"})});export const richText1=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/e(\"p\",{children:\"Aging characterized by a progressive deterioration in functions, tissue structural integrity and physiological regulations across multiple organs is the dominant risk factor of cancer, cardiovascular disease, diabetes, and neurodegenerative disorders. Senescence is a cellular response featured by a permanent proliferation arrest via activation of p16INK4a/Rb and p53/p21CIP1 pathways and other phenotypic alterations, including activation of a so-called senescence-associated secretory phenotype (SASP) with a complexed proinflammatory molecules (Di Micco et al., 2021; Gorgoulis et al., 2019). On the one hand, cellular senescence is involved in diverse biological processes, such as embryonic development, tissue homeostasis, and suppression of tumor progression. On the other hand, senescence has also been revealed as a major cause of aging and age-related diseases. Although it is still controversial on the robustness of using a single and universal biomarkers to identify senescent cells due to heterogeneity of cell and tissue types, \u03B2-galactosidase (\u03B2-gal) positive staining, lack of proliferative marker Ki-67 and p21 expression, when present simultaneously, have been implicated as the common combinatory method to distinguish permanently arrested senescent cells from those pre-senescent, quiescent and differentiated cells. Currently, one major challenge in the field of senescence, especially in aging tissues, is the complexity of geographically uneven distribution of senescent cells and their variable molecular characteristics within and across entropic organ areas. Thus, a potent spatial-transcriptomics method that associates gene expression with cellular spatial-temporal information is essentially needed to dissect the molecular dynamics of senescence cells within their niche and their interactions with the neighboring environment for deepening our understanding and discovering novel therapeutic targets to mitigate the progress of senescence-related aging.\"}),/*#__PURE__*/t(\"p\",{children:[/*#__PURE__*/e(\"br\",{}),\"Stereo-seq empowered by MGI DNA nanoball (DNB) sequencing technology is an \",/*#__PURE__*/e(\"em\",{children:\"in-situ\"}),\" capture-based spatial multi-omic solution. It provides unparalleled subcellular resolution at nm scale and large cm field-of-view to realize high-throughput and simultaneous analysis between gene expression, cellular morphology, microenvironments and protein makers on single cell level. Moreover, a comprehensive benchmarking study on sequencing-based spatial transcriptomic methods (sST) using a spectrum of mouse tissues and regions with the well-defined histological architectures was published recently in Nature methods (You et al., 2024). The study concludes that Stereo-seq is technically the top-ranked spatial solution when systematically compared with other sST methods, by means of spatial resolution, capture efficiency, molecular diffusion and gene detection in both all reads and downsampled data (Fig. 1). Especially, the spatial gene expression profiles generated by Stereo-seq are much more refined and superior to other methods due to its the sub-micrometer spot size, which is much smaller than a single cell. Additionally, Stereo-seq exhibited way lower level of blood contamination in comparison with all Visium and DynaSpatial spots, of which majority express notable level of Hba-a1. \"]}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,VsS0yTDZtFkPFeSm1Ql3bVEOpkg.svg\",\"data-framer-height\":\"22979\",\"data-framer-width\":\"31108\",height:\"11489\",src:\"https://framerusercontent.com/images/VsS0yTDZtFkPFeSm1Ql3bVEOpkg.svg\",srcSet:\"https://framerusercontent.com/images/VsS0yTDZtFkPFeSm1Ql3bVEOpkg.svg?scale-down-to=512 512w,https://framerusercontent.com/images/VsS0yTDZtFkPFeSm1Ql3bVEOpkg.svg?scale-down-to=1024 1024w,https://framerusercontent.com/images/VsS0yTDZtFkPFeSm1Ql3bVEOpkg.svg?scale-down-to=2048 2048w,https://framerusercontent.com/images/VsS0yTDZtFkPFeSm1Ql3bVEOpkg.svg?scale-down-to=4096 4096w,https://framerusercontent.com/images/VsS0yTDZtFkPFeSm1Ql3bVEOpkg.svg 31108w\",style:{aspectRatio:\"31108 / 22979\"},width:\"15554\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Figure 1 \"}),\"(adapted from You et al., 2024). Ranking of the 11 sST methods based on their performance in the specified categories with the highest performing methods positioned at the top. Methods that offer resolution levels below 20\u2009\u03BCm have been given higher preference. In the right panel,essential characteristics of the sST methods examined are outlined. The analyzed issues include mouse embryonic eyes, hippocampal regions of the mouse brain and mouse olfactory bulbs\"]})})}),/*#__PURE__*/e(\"p\",{children:\"By utilizing Stereo-seq\u2019s unprecedent quality and throughput capacity, a fresh paper, spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging, has been published in Cell (Ma, et al., 2024) identifying senescence-sensitive spots (SSSs) and accumulation of immunoglobulin G (IgG) as features of tissue aging and entropy. Nine tissues from both young (2-month) and aged male (25-month) mice were included in this study to generate a systematic and in-depth spatial transcriptomic atlas as the cornerstone to unveil characteristic drivers for aging-related loss of tissue integrity and organ dysfunction. 1,535,191 high-resolution spots from 103 tissue samples with an average of 1,450 genes per spot were obtained using Stereo-seq along with the confirmation of senescent cells in aged mice by the \u03B2-gal staining (Fig. 2). Moreover, 71 distinct spot types matching with known cellular components in different tissues and organs were identified after annotation with marker genes. In addition, a stack of aged-related features, including chronic inflammation revealed by CD45-positive immune cells, enlarged inflammatory areas, increased fibrosis and morphological changes were clearly spotted in the issues from aged mice. Overall, a high-resolution spatial expression platform with stringent controls and validation in aging and senescence has been established in this study for subsequent dissection of underlying molecular mechanisms of aging.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,GYHU79cWraKTYV6o0qK6OST3LE.png\",\"data-framer-height\":\"1003\",\"data-framer-width\":\"774\",height:\"501\",src:\"https://framerusercontent.com/images/GYHU79cWraKTYV6o0qK6OST3LE.png\",srcSet:\"https://framerusercontent.com/images/GYHU79cWraKTYV6o0qK6OST3LE.png 774w\",style:{aspectRatio:\"774 / 1003\"},width:\"387\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Figure 2 \"}),\"(from Ma et al., 2024)\",/*#__PURE__*/e(\"strong\",{children:\".\"}),\" Building a spatial transcriptomic atlas for young and aged male mice across multiple tissues (\",/*#__PURE__*/e(\"strong\",{children:\"A\"}),\") Schematic diagram of tissue sampling, histological analysis, and spatial transcriptomics sequencing in young and old mice. (\",/*#__PURE__*/e(\"strong\",{children:\"B\"}),\") SA-b-Gal analysis of multiple tissues from young (2-month-old) and old (25-month-old) mice (n = 5 per group). Data are presented as the mean \\xb1 SEM. Mann-Whitney test. Scale bars, 50 mm. (\",/*#__PURE__*/e(\"strong\",{children:\"C\"}),\") Spatial transcriptomic atlas of young mouse tissues. The central UMAP plot shows the distribution of spots across various mouse tissues, with surrounding diagrams indicating the respective tissues. Refer to Table S1 for the abbreviation keys. Scale bars in inner plots, 200 mm (lymph node), 500 mm (hippocampus and spinal cord), 1 mm (others). Scale bars in the outer plots, 100 mm (lymph node), 200 mm (others). (\",/*#__PURE__*/e(\"strong\",{children:\"D\"}),\") The proportion of spot types in each tissue from young samples. The dendrogram shows the positional similarity between spot types. The bar plot shows the proportion of different spot types in each tissue, with each dot representing one sample.\"]})})}),/*#__PURE__*/e(\"p\",{children:\"Furthermore, a novel organizational structure entropy (OSE) method developed from those refined granular tissue spatial pictures by Stereo-seq was proposed to quantify aging-related tissue deterioration in this paper, showing scoring is higher in aged male mice in the examined issues and increases with age (4, 13, 19 months) when compared with younger mice (Fig.3). Sub-cellular spatial-solution offered Stereo-seq also provides a possibility to this study for in-depth analysis of differentially expressed genes (DEGs) along age and across different spatial locations, demonstrating DEGs are closely associated with chronic inflammation, mitochondrial dysfunction, disrupted nutrient and apoptosis. Moreover, both spatial and cell-type-specific expression patterns are revealed in a large proportion of aging-related DEGs.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,t4hL4i3U6Wf1ML9kQyV9teI0o.png\",\"data-framer-height\":\"1134\",\"data-framer-width\":\"1026\",height:\"567\",src:\"https://framerusercontent.com/images/t4hL4i3U6Wf1ML9kQyV9teI0o.png\",srcSet:\"https://framerusercontent.com/images/t4hL4i3U6Wf1ML9kQyV9teI0o.png?scale-down-to=1024 926w,https://framerusercontent.com/images/t4hL4i3U6Wf1ML9kQyV9teI0o.png 1026w\",style:{aspectRatio:\"1026 / 1134\"},width:\"513\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Figure 3 \"}),\"(from Ma et al., 2024)\",/*#__PURE__*/e(\"strong\",{children:\".\"}),\" Exploration of structural deterioration and cellular identity loss in aged male mice. (\",/*#__PURE__*/e(\"strong\",{children:\"A\"}),\") Schematic illustrating the organizational structure entropy (OSE) analysis. (\",/*#__PURE__*/e(\"strong\",{children:\"B\"}),\") Violin plots demonstrating the increased OSE score during aging. Wilcoxon rank-sum test. (\",/*#__PURE__*/e(\"strong\",{children:\"C\"}),\") The spatial mappings showing the OSE score in young (2-month-old) and old (25-month-old) mice tissues. Scale bars, 500 and 100 mm (zoom-in) for hippocampus and lymph node, 1 mm and 200 mm (zoom-in) for the spleen. (\",/*#__PURE__*/e(\"strong\",{children:\"D\"}),\") Heatmaps display cell identity scores in aged tissues and regions with high OSE scores. (\",/*#__PURE__*/e(\"strong\",{children:\"E\"}),\") The spatial mappings showing the decreased cell identity scores during aging. Scale bars, 200 mm. (\",/*#__PURE__*/e(\"strong\",{children:\"F\"}),\") Schematic diagram of tissue sampling and spatial transcriptomics sequencing of 4, 13, 19-month-old mice. (\",/*#__PURE__*/e(\"strong\",{children:\"G\"}),\") Spatial mappings showing spot types of 4-month-old mice. Scale bars, 200 and 100 mm (zoom-in) for hippocampus and lymph node, 1 mm and 200 mm (zoom-in) for liver and spleen. (\",/*#__PURE__*/e(\"strong\",{children:\"H\"}),\") Boxplots showing the change of OSE during aging, with each point indicating a superpixel unit. Wilcoxon rank-sum test. (\",/*#__PURE__*/e(\"strong\",{children:\"I\"}),\") Heatmap showing the decreased cell identity scores during aging. (\",/*#__PURE__*/e(\"strong\",{children:\"J\"}),\") Spatial mappings showing the decreased cell identity scores during aging. Scale bars, 200 mm.\"]})})}),/*#__PURE__*/t(\"p\",{children:[\"To dissect senescent cells and their interactions with the neighboring environment, the study proposed the concept of senescence sensitive spot (SSS). A careful characterization of SSS cellular and molecular signatures unveiled that immunoglobulin-producing cells (Ighigh) in various aged tissues preferentially locate in the vicinity of SSS, thus creating a pro-inflammatory niche. In addition, OSE scoring declines with increasing distance from those SSS spots. Intriguingly, the study discovered IgG, a key immune factor increases with age in both male and female mice tissues at different life stages from young to aged, indicating IgG accumulation during aging is a sex-independent universal fact. More importantly, the same findings have been also demonstrated in human issues, including lymph node, liver, spleen, and brain. Consequently, a consistent pattern of intra-tissue IgG elevation observed across multiple life stages, sexes, and species, supporting IgG could be an evolutionarily conserved marker in human and mammals aging. These findings are further reinforced by subsequent \",/*#__PURE__*/e(\"em\",{children:\"in vivo\"}),\" and \",/*#__PURE__*/e(\"em\",{children:\"in vitro\"}),\" physiological studies, showing loss-of-function in terms of clearance of antibody-producing cells, mediated FcRn ablation, or neutralizing IgG\u2019s pro-senescent effects alleviates aging-related tissue deformation and senescence and could even partially revert the aging phenotype in mice. Conversely, \",/*#__PURE__*/e(\"em\",{children:\"in vivo\"}),\" gain-of-function study by long-term IgG administration showed accelerated aging-related defects and senescence in mice, indicating IgG acts as a senescence-promoting factor.\"]}),/*#__PURE__*/t(\"p\",{children:[\"To conclude, a high-precision spatiotemporal profile achieved by Stereo-seq followed by \",/*#__PURE__*/e(n,{href:{webPageId:\"eKjOOMrpl\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"MGI DNB sequencing\"})}),\" on \",/*#__PURE__*/e(n,{href:{pathVariables:{AtJME6gJe:\"dnbseq-t7\"},unresolvedPathSlugs:{AtJME6gJe:{collectionId:\"GoaUlI0s0\",collectionItemId:\"fp0RbRNQ1\"}},webPageId:\"I3WJFR83n\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"DNBSEQ-T7 \"})}),\"delivers unsurpassed spatiotemporal transcriptomic profiles of murine aging, covering various tissues across different chronological stages and forms a concrete foundation forsimilar studies in the future. A thorough spatial transcriptomic landscape in female mammals is the next focus to complete the picture of aging in mammals and to potentially unmask sex-dependent aging mechanisms. Further finetuning on the mechanisms of IgG accumulation in cellular aging could lead to the development of immunoglobulin-targeted therapies to treat age-related diseases.\"]}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"strong\",{children:\"References:\"})}),/*#__PURE__*/t(\"p\",{children:[\"Systematic comparison of sequencing-based spatial transcriptomic methods. You et al., \",/*#__PURE__*/e(\"em\",{children:\"Nature Methods\"}),\", Sep; 21(9):1743-1754, (2024).\"]}),/*#__PURE__*/t(\"p\",{children:[\"Spatial transcriptomic landscape unveils immunoglobin-associated senescence as a hallmark of aging. Ma et al., \",/*#__PURE__*/e(\"em\",{children:\"Cell\"}),\", Nov 2:S0092-8674(24)01201-7, (2024).\"]}),/*#__PURE__*/t(\"p\",{children:[\"Cellular senescence in ageing: from mechanisms to therapeutic opportunities. Di Micco et al., \",/*#__PURE__*/e(\"em\",{children:\"Nat Rev Mol Cell Biol, \"}),\"Feb;22(2):75-95, (2021).\"]}),/*#__PURE__*/t(\"p\",{children:[\"Cellular Senescence: Defining a Path Forward. Gorgoulis et al., \",/*#__PURE__*/e(\"em\",{children:\"Cell,\"}),\" Oct 31;179(4):813-827, (2019).\"]})]});export const richText2=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/t(\"p\",{children:[\"Explore advanced detection of drug-resistant \",/*#__PURE__*/e(\"em\",{children:\"Mycobacterium tuberculosis\"}),\" mutations using ELITe InGenius\\xae and MGI ATOPlex DNBSEQ\u2122 technologies for faster, more accurate diagnosis.\"]})});export const richText3=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/e(\"h2\",{children:\"Introduction\"}),/*#__PURE__*/e(\"p\",{children:\"Tuberculosis (TB) is a preventable and transmittable disease. It is one of the top 10 causes of death worldwide and the world\u2019s second (after COVID-19) cause of death from a single infectious agent, ranking above HIV/AIDS. TB is caused by the bacillus Mycobacterium tuberculosis (MTB), which spreads through microdroplets expelled by affected people in the air, for instance when coughing. The disease primarily affects the lungs (pulmonary TB) but can also affect other organs or human tissues (extrapulmonary TB) in about 15% of cases. It is estimated that about a quarter of the world\u2019s population is infected with latent tuberculosis. The number of new TB cases has been decreasing slowly over the past years. In 2022, 10 million people developed tuberculosis, and out of these approximately 8% were people living with HIV.\"}),/*#__PURE__*/e(\"p\",{children:\"When early detected and appropriately treated, TB is curable. However, globally in 2022, the total number of deaths caused by TB was 1.13 million in HIV-negative people and 167,000 in HIV-positive people. The World Health Organization (WHO) reported 19% net reduction in the global number of deaths caused by TB from 2015 to 2022. Though valuable, the result is still far from the milestone of a 75% reduction by 2025, set in the WHO\u2019s End TB strategy. The early diagnosis of TB, including universal drug-susceptibility testing and treatment of all people with TB are among the pillars of such WHO Strategy (WHO Global Tuberculosis Report 2022).\"}),/*#__PURE__*/e(\"p\",{children:\"Drug-resistant tuberculosis (DR-TB) is a major challenge in the containment and eradication of TB. Drug resistance arises as a consequence of the use of anti-TB drugs, especially first-line drugs, like rifampicin and isoniazid. Rifampicin works by inhibiting bacterial DNA-dependent RNA polymerase, encoded by the rpoB gene (Hartmann 1967). Resistance to this drug has been associated mainly with mutations in a limited region of the rpoB gene (Telenti 1993). Multidrug-resistant TB (MDR-TB), which is resistant to both isoniazid and rifampicin, represents a serious concern in 2022, 410000 people were estimated to have developed MDR-TB or RR-TB.\"}),/*#__PURE__*/e(\"p\",{children:\"Although culture is the gold standard test for diagnosis of TB, it requires more resources and takes longer to produce results (2\u20136 weeks) than molecular tests and sputum-smear microscopy, which can provide results in less than 1 day. Accurate and rapid detection of TB and MDR-TB is critical for improving patient care and decreasing disease transmission. Therefore, the WHO recommends using diagnostic molecular tests as the first approach for detecting TB among people with signs or symptoms. Nonetheless, despite the improvements, current molecular tests for MDR-TB can only detect a limited number of resistant mutations and their upgrade lies behind the continuous rise of new resistant mutations. In this picture, high throughput sequencing (NGS) is a technology that is gaining interest for the detecting multi and extensively drug-resistant (MDR-TB and XDR-TB) TB strains, since it provides the possibility to investigate a high number of sites within different genes carrying mutations that lead to resistance.\"}),/*#__PURE__*/e(\"p\",{children:\"This pilot study presents preliminary data on a complete NGS workflow (Fig. 1) for TB resistances detection. It was designed to exploit the good synergy between two complementary solutions: ELITech Group MDR/MTB ELITe MGB\\xae Kit assay on the ELITe InGenius integrated platform for nucleic acids preparation and the MGI ATOPlex DNA Custom Library protocol analyzed with DNBSEQTM sequencing technology:\"}),/*#__PURE__*/e(\"p\",{children:\"\u2022\\xa0\\xa0\\xa0\\xa0 ELITEch Group ELITe InGenius instrument is a sample-to-result technology able to perform nucleic acid extraction, Real-Time PCR (RT-PCR) using MDR/MTB ELITe MGB\\xae Kit and results interpretation to identify MTB positive clinical samples (sputum) as a first diagnostic test.\"}),/*#__PURE__*/e(\"p\",{children:\"\u2022\\xa0\\xa0\\xa0\\xa0 MGI ATOPlex DNA Custom Library is a targeted multiplex PCR-based enrichment technique that, in combination with the high performance of DNBSEQTM sequencing technology, allows interrogate simultaneously whole MTB genes sequences to better characterize the genotypic drug-resistance also for mutation unknown.\"}),/*#__PURE__*/e(\"p\",{children:\"This all-in-one workflow could be easily adapted and validated at any laboratory, to obtain a rapid and multi comprehensive solution for the detection of TB drug resistance.\"}),/*#__PURE__*/e(\"h2\",{children:\"Experimental Methods\"}),/*#__PURE__*/e(\"h3\",{children:\"ELITe Ingenius \u2013 ATOPlex DNBSEQTM Workflow\"}),/*#__PURE__*/e(\"p\",{children:\"The complete workflow allows an initial identification of MTB-positive sputum using MDR/MTB ELITe MGB\\xae Kit Real-Time PCR assay on the automated sample-to-results instrument ELITe InGenius. Positive samples are subsequently analyzed with a downstream process of targeted analysis by amplicon sequencing using the MGI ATOPlex DNA Custom Library technology. The samples are then sequenced on the MGI DNBSEQTM sequencing platform. The data are finally analyzed through a bioinformatics pipeline to detect potential mutations in different target genes that might be implicated in resistance to pharmacological treatments against M. tuberculosis (Fig.1).\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,3LUbLsK04EwKZkClMjgqvi0hz4.jpg\",\"data-framer-height\":\"758\",\"data-framer-width\":\"1514\",height:\"379\",src:\"https://framerusercontent.com/images/3LUbLsK04EwKZkClMjgqvi0hz4.jpg\",srcSet:\"https://framerusercontent.com/images/3LUbLsK04EwKZkClMjgqvi0hz4.jpg?scale-down-to=512 512w,https://framerusercontent.com/images/3LUbLsK04EwKZkClMjgqvi0hz4.jpg?scale-down-to=1024 1024w,https://framerusercontent.com/images/3LUbLsK04EwKZkClMjgqvi0hz4.jpg 1514w\",style:{aspectRatio:\"1514 / 758\"},width:\"757\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Figure 1\"}),\": Complete workflow created from nucleic acid extraction, Real-Time PCR analysis on the ELITe InGenius\\xae, NGS with the DNBSEQTM technology, and data analysis with custom bioinformatic pipeline.\"]})})}),/*#__PURE__*/e(\"h2\",{children:\"\\xa0Samples Preparation\"}),/*#__PURE__*/e(\"p\",{children:\"Different sample types were used to test the whole process. Commercial M. Tuberculosis H37Rv genomic DNA (ATCC) was used as a positive control. Ultrapure water was used as a negative control. Simulated samples were prepared by adding inactivated MTB isolates obtained from external providers at concentrations of 105 CFU/mL and 104 CFU/mL to MTB-negative sputum specimens. Two different strains were tested: one wild type (H37Rv) and one carrying a mutation on the katG gene (codon 315, responsible for isoniazid resistance). Moreover, three inactivated clinical isolates from TB-positive sputum samples obtained from external providers were also processed. The three samples were characterized using MDR/MTB ELITe MGB\\xae Kit on ELITe InGenius\\xae, one was detected as MTB wild-type, while the other two were detected as MTB mutated respectively for katG and rpoB (codon 526, responsible for rifampicin resistance). In total, 11 samples plus a positive control were tested to evaluate the whole process for sequencing analysis.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,MPN6oxbYdrm5wKA620KuBbvq0.png\",\"data-framer-height\":\"679\",\"data-framer-width\":\"822\",height:\"339\",src:\"https://framerusercontent.com/images/MPN6oxbYdrm5wKA620KuBbvq0.png\",srcSet:\"https://framerusercontent.com/images/MPN6oxbYdrm5wKA620KuBbvq0.png?scale-down-to=512 512w,https://framerusercontent.com/images/MPN6oxbYdrm5wKA620KuBbvq0.png 822w\",style:{aspectRatio:\"822 / 679\"},width:\"411\"}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Table 1\"}),\": Samples List with number of replicates, concentrations, and type of specimen.\"]})}),/*#__PURE__*/e(\"h4\",{children:\"DNA extraction and Real-Time PCR analysis\"}),/*#__PURE__*/e(\"p\",{children:\"Samples DNA extraction and Real-Time PCR analysis to detect MTB positivity were performed using the ELITe InGenius\\xae platform and MDR/MTB ELITe MGB\\xae Kit. Through a preload assay, the instrument extracts DNA from fluidified, decontaminated, and inactivated sputum samples and subsequently performs the Real-Time PCR analysis to detect the presence of MTB (IS6110 target gene). The process uses just a minimal part of the extracted DNA sample (40 \\xb5l out of 100 \\xb5l of total eluate volume) and after the extraction, the remaining eluate volume was utilized to prepare sequencing libraries for the DNBSEQTM sequencing.\"}),/*#__PURE__*/t(\"h4\",{children:[/*#__PURE__*/e(\"em\",{children:\"\\xa0\"}),\"Library preparation for sequencing\"]}),/*#__PURE__*/e(\"p\",{children:\"Sequencing libraries were prepared using the MGI ATOPlex DNA Custom Library kit, which contains all reagents and controls required for the optimized ATOPlex DNBSEQTM workflow. The process starts with a first PCR step to amplify the target gene regions, and a second PCR step to amplify the dual-barcode bacterial genome. In this study, 5 target genes associated with resistance to first-line drugs were investigated. The MTB strain present in the sample was predicted to be susceptible or resistant to each antibiotic or with yet-to-be-characterized mutations, based on the absence or presence of mutations in these loci, the target gene region and interrogation of reference databases. For each gene, the full protein-coding regions and some upstream and downstream bases were covered to maximize the discovery of known and novel variants (Table 2).\"}),/*#__PURE__*/e(\"p\",{children:\"All target regions were covered, and 116 primer pairs were designed.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,pXCGARYhZtVa53POODH9VjWf8.png\",\"data-framer-height\":\"111\",\"data-framer-width\":\"992\",height:\"55\",src:\"https://framerusercontent.com/images/pXCGARYhZtVa53POODH9VjWf8.png\",srcSet:\"https://framerusercontent.com/images/pXCGARYhZtVa53POODH9VjWf8.png?scale-down-to=512 512w,https://framerusercontent.com/images/pXCGARYhZtVa53POODH9VjWf8.png 992w\",style:{aspectRatio:\"992 / 111\"},width:\"496\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/t(\"p\",{children:[/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Table 2\"}),\": Summary of target genes, function, major drug resistance and gene size covered (bp) with the MGI ATOPlex DNA Custom library protocol\"]}),\".\"]})}),/*#__PURE__*/e(\"p\",{children:\"After the dsDNA libraries preparation, the specific steps to MGI's DNBSEQTM technology were followed. At first, the quality of the dsDNA libraries was evaluated using the Agilent Tapestation platform. All samples showed good quality and were used to perform the circularization of single-strand DNA to produce the ssCircDNA libraries. The ssCircDNA libraries obtained were then converted to DNA Nanoballs (DNB) for sequencing.\"}),/*#__PURE__*/e(\"p\",{children:\"A negative control (ultrapure water) sample was introduced during the MGI ATOPlex DNA Custom Library preparation as a cross-contamination control during this step.\"}),/*#__PURE__*/e(\"h2\",{children:\"Sequencing\"}),/*#__PURE__*/e(\"p\",{children:\"DNB sequencing takes place on high-density pattern arrays that immobilize individual DNBs for highly parallelized tracking of dNTP incorporation during strand extension (Figure 2). Sequencing starts with the hybridization of DNBs to anchor spots. After primer hybridization, the flow cell is flushed with fluorescently labeled dNTP probes. Unbound probes are washed away, and bound probes are stimulated to fluoresce. High resolution imaging and proprietary algorithms transform signals into high-quality and highly accurate sequencing results.\"}),/*#__PURE__*/e(\"p\",{children:\"Libraries were pooled and sequenced using the DNBSEQ-G400 sequencer and the FCL PE100 sequencing set with dual barcode sequencing, according to the instructions of the ATOPlex protocol. Four pools of samples were sequenced in each run. The four pools were loaded manually into the four lanes of the flow cell using the MGIDL-200H Portable DNB Loader since they shared the same barcodes.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,nZuGrTgohCZ1uHN5V7j0JJHuYsA.png\",\"data-framer-height\":\"915\",\"data-framer-width\":\"1240\",height:\"457\",src:\"https://framerusercontent.com/images/nZuGrTgohCZ1uHN5V7j0JJHuYsA.png\",srcSet:\"https://framerusercontent.com/images/nZuGrTgohCZ1uHN5V7j0JJHuYsA.png?scale-down-to=512 512w,https://framerusercontent.com/images/nZuGrTgohCZ1uHN5V7j0JJHuYsA.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/nZuGrTgohCZ1uHN5V7j0JJHuYsA.png 1240w\",style:{aspectRatio:\"1240 / 915\"},width:\"620\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Figure 2\"}),\": DNB sequencing uses the combinatory probe anchor system (cPAS) on a patterned array \uFB02ow cell to provide high sequencing accuracy with improved imaging and reduced index hopping.\"]})})}),/*#__PURE__*/e(\"h2\",{children:\"Data analysis and sequencing assembly\"}),/*#__PURE__*/e(\"p\",{children:\"The sequencing data quality control check was performed using the FastQC software. SOAPnuke software was used to remove low-quality reads and adapter sequences from the Fasta output file. Minimap2 software was used to align reads to the MTB H37Rv reference genome (NC_000962.3), and then variants were called using the GATK HaplotypeCaller software. BAM file quality and mapping statistics were assessed using Bamstats software. Data generated was visualized using IVG software.\"}),/*#__PURE__*/e(\"h2\",{children:\"\\xa0Results\\xa0\"}),/*#__PURE__*/e(\"p\",{children:\"All samples prepared were successfully processed with ELITech Group ELITe InGenius\\xae platform and MDR/MTB ELITe MGB\\xae Kit, as confirmed by the amplification of the internal Control of the assay. Analysis of the real-time PCR amplification and melting curve analysis confirmed the expected MTB genotype. An example of Real-Time PCR output from ELITe InGenius\\xae is shown in Fig.3.\"}),/*#__PURE__*/e(\"p\",{children:\"The remaining MTB DNA eluates from all samples were used to prepare sequencing libraries on the DNBSEQ-G400 sequencer. The libraries displayed good quality and concentration, resulting in appropriateness for the sequencing analysis. All samples were analyzed together in a single pool in 4 replicates across 4 different sequencing lanes of the DNBSEQ-G400 flowcell.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,Q74o5iisd91FvEkm0vpDMeGiaw.jpg\",\"data-framer-height\":\"771\",\"data-framer-width\":\"1505\",height:\"385\",src:\"https://framerusercontent.com/images/Q74o5iisd91FvEkm0vpDMeGiaw.jpg\",srcSet:\"https://framerusercontent.com/images/Q74o5iisd91FvEkm0vpDMeGiaw.jpg?scale-down-to=512 512w,https://framerusercontent.com/images/Q74o5iisd91FvEkm0vpDMeGiaw.jpg?scale-down-to=1024 1024w,https://framerusercontent.com/images/Q74o5iisd91FvEkm0vpDMeGiaw.jpg 1505w\",style:{aspectRatio:\"1505 / 771\"},width:\"752\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Figure 3\"}),\": Example of Real-Time PCR results obtained with the ELITe InGenius\\xae instrument.\"]})})}),/*#__PURE__*/e(\"p\",{children:\"The sequencing process generated an output of 386M clean reads for all the samples analyzed, achieving a Phred Quality Score of 30 (Q30) of 95% or higher. This demonstrates how the complete process was able to produce a considerable amount of data with an excellent quality of base call accuracy, as most of the reads obtained were able to reach a Q30 score. In addition, 128M reads out of the 386M reads obtained were successfully mapped to the expected rpoB, katG, inhA, gyrA and embB gene targets (Table 3).\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,oQ3dSTMS8DkwQXhDMv8te84AQ.png\",\"data-framer-height\":\"748\",\"data-framer-width\":\"823\",height:\"374\",src:\"https://framerusercontent.com/images/oQ3dSTMS8DkwQXhDMv8te84AQ.png\",srcSet:\"https://framerusercontent.com/images/oQ3dSTMS8DkwQXhDMv8te84AQ.png?scale-down-to=512 512w,https://framerusercontent.com/images/oQ3dSTMS8DkwQXhDMv8te84AQ.png 823w\",style:{aspectRatio:\"823 / 748\"},width:\"411\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Table 3\"}),\": Overall total reads count and Q30% for the samples analyzed.\"]})})}),/*#__PURE__*/e(\"p\",{children:\"An overview of the overall average depth obtained for all samples analyzed across the MTB genome is represented in Figure 3. The data obtained from the targeted sequencing process fell in the genome region of the 5 target genes as expected, moreover the number of reads generated was sufficient to detect the presence of mutations with extremely high precision. Mutations in katG and rpoB genes were properly detected and visualized with the IVG genome browser as shown in Fig.4 A and B.\"}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"strong\",{children:\"A)\"})}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,8IuwqFr7fl5U6rutfeR7SrGgJg.png\",\"data-framer-height\":\"672\",\"data-framer-width\":\"1032\",height:\"336\",src:\"https://framerusercontent.com/images/8IuwqFr7fl5U6rutfeR7SrGgJg.png\",srcSet:\"https://framerusercontent.com/images/8IuwqFr7fl5U6rutfeR7SrGgJg.png?scale-down-to=512 512w,https://framerusercontent.com/images/8IuwqFr7fl5U6rutfeR7SrGgJg.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/8IuwqFr7fl5U6rutfeR7SrGgJg.png 1032w\",style:{aspectRatio:\"1032 / 672\"},width:\"516\"}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"strong\",{children:\"B)\"})}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,Bc1sh5taZxDFWFhVOHlmXUxd1z0.png\",\"data-framer-height\":\"1089\",\"data-framer-width\":\"1028\",height:\"544\",src:\"https://framerusercontent.com/images/Bc1sh5taZxDFWFhVOHlmXUxd1z0.png\",srcSet:\"https://framerusercontent.com/images/Bc1sh5taZxDFWFhVOHlmXUxd1z0.png?scale-down-to=1024 966w,https://framerusercontent.com/images/Bc1sh5taZxDFWFhVOHlmXUxd1z0.png 1028w\",style:{aspectRatio:\"1028 / 1089\"},width:\"514\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Figure 4\"}),\": Example of sequencing data distribution across the MTB genome and of a visualization of mutation on katG and rpoB target genes. A) Each peak shows the sequencing depth of each target gene of the panel tested across the MTB genome. B) Extract of the alignment of the sequencing data on the IVG genome browser and visualization of the mutations detected.\"]})})}),/*#__PURE__*/e(\"p\",{children:\"The analysis with GATK HaplotypeCaller allowed the precise detection of the genotype of each one of the 11 samples analyzed with the MGI ATOPlex protocol. All simulated MTB wild-type samples (samples 2-5) were confirmed at different concentrations and replicates, likewise, simulated MTB-resistant samples (samples 6-9) were properly detected and the expected mutation pattern was confirmed across different concentrations and replicates. Similarly, the sequencing analysis allowed the identification of the expected genotype in the three clinical isolates (samples 10-12) and the identification of new unknown mutations not reported as related to drug resistance. A summary of the results obtained is reported in Table.4.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,5stwoGcCFWcHqxJGiUM2jFSGmBo.png\",\"data-framer-height\":\"1094\",\"data-framer-width\":\"760\",height:\"547\",src:\"https://framerusercontent.com/images/5stwoGcCFWcHqxJGiUM2jFSGmBo.png\",srcSet:\"https://framerusercontent.com/images/5stwoGcCFWcHqxJGiUM2jFSGmBo.png?scale-down-to=1024 711w,https://framerusercontent.com/images/5stwoGcCFWcHqxJGiUM2jFSGmBo.png 760w\",style:{aspectRatio:\"760 / 1094\"},width:\"380\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,Kp5AC27Mrym3BeathT5oshCHCk.png\",\"data-framer-height\":\"1094\",\"data-framer-width\":\"761\",height:\"547\",src:\"https://framerusercontent.com/images/Kp5AC27Mrym3BeathT5oshCHCk.png\",srcSet:\"https://framerusercontent.com/images/Kp5AC27Mrym3BeathT5oshCHCk.png?scale-down-to=1024 712w,https://framerusercontent.com/images/Kp5AC27Mrym3BeathT5oshCHCk.png 761w\",style:{aspectRatio:\"761 / 1094\"},width:\"380\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,y8cfcUwjEPOj9Bo5MKzS77jr4o.png\",\"data-framer-height\":\"430\",\"data-framer-width\":\"758\",height:\"215\",src:\"https://framerusercontent.com/images/y8cfcUwjEPOj9Bo5MKzS77jr4o.png\",srcSet:\"https://framerusercontent.com/images/y8cfcUwjEPOj9Bo5MKzS77jr4o.png?scale-down-to=512 512w,https://framerusercontent.com/images/y8cfcUwjEPOj9Bo5MKzS77jr4o.png 758w\",style:{aspectRatio:\"758 / 430\"},width:\"379\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/t(\"em\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Table 4\"}),\": Summary of the detected variant call. For each sample, the detected mutations are listed according to position on the MTB genome, gene name, reference/alternate base and quality score called by GATK HaplotypeCaller. Expected mutations of each sample are reported in bold. Additional unknown mutations were also detected during the analysis.\"]})})}),/*#__PURE__*/e(\"h3\",{children:\"Conclusion\"}),/*#__PURE__*/e(\"p\",{children:\"This preliminary study shows the applicability of a time-to-result-effective workflow for NGS sequencing, combining Elitechgroup ELITech Group ELITe InGenius\\xae system for extraction and Real-Time PCR analysis and together with MGI ATOPlex DNBSEQTM sequencing technology. This workflow potentially constitutes a solution to investigate the genotypic drug resistance of M. tuberculosis, providing a substantial improvement over the current MTB diagnosis. Compared to other systems available on the market, the combination of the ELITe InGenius\\xae\u2019 sample-to-results technology with ATOPlex's targeted sequencing technology, substantially reduces the time and cost of analysis of clinical MTB samples, limiting at the same time the manual activity of the operator.\"}),/*#__PURE__*/e(\"p\",{children:\"This newly established process may allow the identification of multiple resistant mutations on samples at different bacillary titres, on the same sample used for the diagnosis of TB, without the need for a second specimen. The complete workflow can be completed in 48 hours and it allows the identification of samples positive for MTB via Real-time PCR analysis in 3 hours using the MDR/MTB ELITe MGB\\xae Kit. The leftover eluate from the same samples collected after Real-Time PCR analysis can be used to perform downstream targeted sequencing for genotypic mutation identification, thus keeping the amount of biological sample to be taken from the patients limited.\"}),/*#__PURE__*/e(\"p\",{children:\"With the experiment performed the leftover of the MTB DNA extracted with the ELITe InGenius\\xae was subsequently used to perform a targeted sequencing analysis using the MGI ATOPlex DNA Custom library preparation protocol and sequenced with DNBSEQ-G400 sequencing instrument. The whole process yielded a consistent amount of high-quality data. Cleaned data were used to analyze the genotype of the genes targeted with MGI ATOPlex protocol, as a final result the expected genotype has been confirmed for all samples. The sequencing process was able to provide details of the single base variants detected in the MTB mutated samples.\"}),/*#__PURE__*/e(\"p\",{children:\"The ATOPlex sequencing DNA Custom Library protocol was developed and designed to cover the entire sequence of the genes that are implicated in the drug-resistance mechanism of MTB, allowing in a single assay the analysis of a large number of codons known to be implicated in drug treatment resistance when carrying mutations. In addition, this new method also allows the detection of potential new mutations on the target genes that can be monitored to evaluate their possible link to unexpected events of drug resistance not associated with known mutations.\"}),/*#__PURE__*/e(\"p\",{children:\"Further investigations are required to assess the whole process performances, such as the limit of sensibility, the precision of analysis with additional mutations (inhA, gyrA, embB) and the performances of the whole process in combination with clinical samples. Also, the applicability to other sequencing systems, like the smaller platforms DNBSEQ-G99 and DNBSEQ-E25, should be tested. The performance evaluation will be crucial for the usability of the workflow for diagnostic purposes. Nonetheless, the easiness of use and the reduced turnaround time to result constitute an unrivalled advantage in disseminating the use of NGS into molecular diagnostics laboratories.\"}),/*#__PURE__*/e(\"h4\",{children:\"References\"}),/*#__PURE__*/t(\"ol\",{children:[/*#__PURE__*/e(\"li\",{\"data-preset-tag\":\"p\",children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(n,{href:\"World Health Organization (WHO)\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"World Health Organization (WHO) Global tuberculosis report 2022.\"})})})}),/*#__PURE__*/e(\"li\",{\"data-preset-tag\":\"p\",children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(n,{href:\"https://www.sciencedirect.com/science/article/abs/pii/0005278767901475?via%3Dihub\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"Hartmann G, Honikel KO, Kn\\xfcsel F, N\\xfcesch J. The specific inhibition of the DNA-directed RNA synthesis by rifamycin. Biochim Biophys Acta. 1967;145(3):843-4. doi: 10.1016/0005-2787(67)90147-5. PMID: 4863911.\"})})})}),/*#__PURE__*/e(\"li\",{\"data-preset-tag\":\"p\",children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(n,{href:\"https://journals.asm.org/doi/10.1128/jcm.31.2.175-178.1993\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"Telenti A, Marchesi F, Balz M, Bally F, B\\xf6ttger EC, Bodmer T. Rapid identification of mycobacteria to the species level by polymerase chain reaction and restriction enzyme analysis. J Clin Microbiol. 1993 Feb;31(2):175-8. doi: 10.1128/jcm.31.2.175-178.1993. PMID: 8381805; PMCID: PMC262730\"})})})})]}),/*#__PURE__*/e(\"h5\",{children:\"Ordering information\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,XHWQcKvVlonghMG8ju5IeFe3us.png\",\"data-framer-height\":\"554\",\"data-framer-width\":\"757\",height:\"277\",src:\"https://framerusercontent.com/images/XHWQcKvVlonghMG8ju5IeFe3us.png\",srcSet:\"https://framerusercontent.com/images/XHWQcKvVlonghMG8ju5IeFe3us.png?scale-down-to=512 512w,https://framerusercontent.com/images/XHWQcKvVlonghMG8ju5IeFe3us.png 757w\",style:{aspectRatio:\"757 / 554\"},width:\"378\"}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"em\",{children:\"*Unless otherwise informed, this StandardMPS sequencing reagent is not available in Germany, UK, Sweden, and Switzerland\"})}),/*#__PURE__*/e(\"h4\",{children:\"Authors\"}),/*#__PURE__*/t(\"p\",{children:[\"Laura Pellegrino, Walter Carbone, Alessandro Abramo, Ludwig Eurlings, Bret Wurdeman, Francesco Gorreta\",/*#__PURE__*/e(\"br\",{}),/*#__PURE__*/e(\"br\",{className:\"trailing-break\"})]})]});export const richText4=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Exploring the intersection of genomics and metabolomics to uncover genetic links with metabolic diseases like diabetes, obesity, and cardiovascular disorders.\"})});export const richText5=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/t(\"p\",{children:[\"Metabolic syndrome is pathologically featured by a broad range of diseases, such as obesity, type 2 diabetes (T2D), cardiovascular and non-acholic fatty liver diseases (NAFLD), resulting from various genetic and acquired disturbances on energy homeostasis.\",/*#__PURE__*/e(\"br\",{}),/*#__PURE__*/e(\"br\",{}),/*#__PURE__*/e(\"br\",{className:\"trailing-break\"})]}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,dZvOdn4rtbuk0qO8S6p55ULTx4.png\",\"data-framer-height\":\"504\",\"data-framer-width\":\"898\",height:\"252\",src:\"https://framerusercontent.com/images/dZvOdn4rtbuk0qO8S6p55ULTx4.png\",srcSet:\"https://framerusercontent.com/images/dZvOdn4rtbuk0qO8S6p55ULTx4.png?scale-down-to=512 512w,https://framerusercontent.com/images/dZvOdn4rtbuk0qO8S6p55ULTx4.png 898w\",style:{aspectRatio:\"898 / 504\"},width:\"449\"}),/*#__PURE__*/e(\"p\",{children:\"During the last decades, the number of people who suffer from those metabolic diseases has been skyrocketing worldwide, becoming an unaffordable burden on both individual health and society. Word Health organization reported that 2.5 billion adults are overweight or obese. Notably, childhood and adolescent obesity have been increasing to an altering level, affecting 390 million population aged between age 5 and 19 years.\\xa0 Moreover, there are approximately 462 million people who had type 2 diabetes reported in 2017 and this number is expected to be increased to 640 million by 2030. Furthermore, the death toll directly caused or associated with those metabolic disorders has been top ranked right after cancer and infectious diseases. In 2019, diabetes and its associated kidney disease caused an estimated 2 million deaths globally. Undoubtably, these alarming facts have been showing the necessity of new treatment development based on further understanding on the disease mechanisms, and more essentially to implement accurate prognosis methods for early intervention especially for those who are inheritably susceptible towards those metabolic diseases.\\xa0\"}),/*#__PURE__*/t(\"p\",{children:[\"In the last two decades, a large number of human genome-wide association studies (hGWAS) have been conducted with the significant lift by the advances in the sequencing-based technologies from microarray to whole-genome and paneled WES sequencing, to statistically establish the association of genetic loci copy number or sequence variations with a specific trait or disease. By great effort, many novel genetic loci and variants have been successfully identified as the potential risk factors for those prevailing metabolic diseases, such as \",/*#__PURE__*/e(\"em\",{children:\"FTO\"}),\" gene polymorphisms linked with physical activity and appetite in several ethnicities (Harbron, J et al., \",/*#__PURE__*/e(\"em\",{children:\"Nature\"}),\", 2014), one SNP in \",/*#__PURE__*/e(\"em\",{children:\"APOA1 \"}),\"gene cluster involved in lipid metabolism (Kristiansson K., \",/*#__PURE__*/e(\"em\",{children:\"Circ Cardiovasc Genet\"}),\", 2012), and the transcript factor \",/*#__PURE__*/e(\"em\",{children:\"TCF7L2\"}),\" \\xa0strongly couples with T2D in multiple populations (Sladek et al., \",/*#__PURE__*/e(\"em\",{children:\"Nature\"}),\", 2007) by regulating beta-cell functions and insulin secretion. However, most of those loci were shown later to have ambiguous direct causal relationship with diseases by more detailed physiological studies, hampering the interpretation of the biological meaning of hGWAS results. Such results also clearly indicate a third factor is indispensably needed to reinforce and validate the association between genetics and eventual biological outcomes.\"]}),/*#__PURE__*/t(\"p\",{children:[\"To fill the missing pieces in this puzzle, the utilization of high-throughput metabolomics platforms on hGWAS analyses has set up a new foundation to better understand and to establish the pathogenic architecture of how genetic variants and pathways influence biological mechanisms and complex diseases, associating genetic information with changes on metabolic profiling. By the light of this methodology, a comprehensive genome-wide characterization study using circulating metabolic biomarkers in blood has been published in \",/*#__PURE__*/e(\"em\",{children:\"Nature\"}),\" (Karjalainen M, et al, 2024) recently by an international collaboration of geneticists, in which more than 400 independent loci and assign putative causal genes were identified by analyzing 233 NMR-quantified circulating metabolic trats in 136,016 participants from a large cohort.\"]}),/*#__PURE__*/e(\"p\",{children:\"\\xa0\"}),/*#__PURE__*/t(\"p\",{children:[\"Besides those previously reported loci associated with \\xa0adiposity and T2D risk, a number of novel loci and probable causal genes in both lipid and glucose homeostasis have been newly discovered by the manual curation and clustering approaches in this study, including \",/*#__PURE__*/e(\"em\",{children:\"LDL-RAP1\"}),\"\u2019s association with lipoprotein and fatty acids, amino acid-associated \",/*#__PURE__*/e(\"em\",{children:\"SLC2A4RG\"}),\" and \",/*#__PURE__*/e(\"em\",{children:\"KCNK16 \"}),\"loci having\\xa0 a regulatory role in activation of insulin-dependent glucose transporter \",/*#__PURE__*/e(\"em\",{children:\"GLUT4\"}),\" and is T2D susceptible gene, respectively.\\xa0 Moreover, a detailed metabolic profiling of lipoprotein- and lipid-associated variants was performed to deepen the understanding of how known and novel loci affect lipoprotein metabolism at a granular level. The clustering produced seven major categories of loci related to the risks in T2D, obesity and lipoprotein particles regulation. Despite those known loci-lipid traits correlations, \",/*#__PURE__*/e(\"em\",{children:\"TRIM5\"}),\" best known for its antiviral role was identified by its lead variant (rs11601507) to associate with 42 lipoprotein and lipid traits, and thus potentially regulate lipid accumulation and inflammation via LDL receptor pathway and \",/*#__PURE__*/e(\"em\",{children:\"mTORC1\"}),\" signaling. This new finding could raise the possibility of using \",/*#__PURE__*/e(\"em\",{children:\"TRIM5\"}),\" as a therapeutical target to reduce the risk of cardiovascular diseases like \",/*#__PURE__*/e(\"em\",{children:\"PCSK9\"}),\" inhibition, especially for statin intolerant individuals. To further demonstrate the values of combining the metabolic association information with disease associations proposed by this study, the associations of intrahepatic cholestasis of pregnancy (ICP) loci with metabolic traits was dissected into detail. The results identified nine replicated and three novel loci (\",/*#__PURE__*/e(\"em\",{children:\"UGT8, NUP153 and HKDC1\"}),\"), not only providing more insights on the metabolic groundwork of inadequately understood diseases like ICP, but also the new findings could be extrapolated into new treatments. Notably, ten loci with only one reported previously have been identified to have robust association with acetone indicated by Mendelian randomization analysis in the study and the positive association with hypertension can be robustly revealed by using only four loci (\",/*#__PURE__*/e(\"em\",{children:\"HMGCS2\"}),\", \",/*#__PURE__*/e(\"em\",{children:\"OXCT1\"}),\", \",/*#__PURE__*/e(\"em\",{children:\"CYP2E1 \"}),\"and \",/*#__PURE__*/e(\"em\",{children:\"SLC2A4/Glut4\"}),\"), enriching the genetic basis of the causal relationship between acetone with hypertension. Last but not least, the study emphasized the sample and participant characteristics, such as serum or plasma, fasting or non-fasting state, can have profound effects on the outcomes of those genetic association analyses.\"]}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,D2mhvNfrMKm2vmXsgBzxfldqXg0.png\",\"data-framer-height\":\"870\",\"data-framer-width\":\"1308\",height:\"435\",src:\"https://framerusercontent.com/images/D2mhvNfrMKm2vmXsgBzxfldqXg0.png\",srcSet:\"https://framerusercontent.com/images/D2mhvNfrMKm2vmXsgBzxfldqXg0.png?scale-down-to=512 512w,https://framerusercontent.com/images/D2mhvNfrMKm2vmXsgBzxfldqXg0.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/D2mhvNfrMKm2vmXsgBzxfldqXg0.png 1308w\",style:{aspectRatio:\"1308 / 870\"},width:\"654\"}),/*#__PURE__*/e(\"p\",{children:\"diseases by more detailed physiological studies, hampering the interpretation of the biological meaning of hGWAS results. Such results also clearly indicate a third factor is indispensably needed to reinforce and validate the association between genetics and eventual biological outcomes.\"}),/*#__PURE__*/t(\"p\",{children:[\"To fill the missing pieces in this puzzle, the utilization of high-throughput metabolomics platforms on hGWAS analyses has set up a new foundation to better understand and to establish the pathogenic architecture of how genetic variants and pathways influence biological mechanisms and complex diseases, associating genetic information with changes on metabolic profiling. By the light of this methodology, a comprehensive genome-wide characterization study using circulating metabolic biomarkers in blood has been published in \",/*#__PURE__*/e(\"em\",{children:\"Nature\"}),\" (Karjalainen M, et al, 2024) recently by an international collaboration of geneticists, in which more than 400 independent loci and assign putative causal genes were identified by analyzing 233 NMR-quantified circulating metabolic trats in 136,016 participants from a large cohort.\"]}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,btd1xQGFjenLezCgIrHLZuzhno.png\",\"data-framer-height\":\"827\",\"data-framer-width\":\"921\",height:\"413\",src:\"https://framerusercontent.com/images/btd1xQGFjenLezCgIrHLZuzhno.png\",srcSet:\"https://framerusercontent.com/images/btd1xQGFjenLezCgIrHLZuzhno.png?scale-down-to=512 512w,https://framerusercontent.com/images/btd1xQGFjenLezCgIrHLZuzhno.png 921w\",style:{aspectRatio:\"921 / 827\"},width:\"460\"}),/*#__PURE__*/t(\"p\",{children:[\"Besides those previously reported loci associated with \\xa0adiposity and T2D risk, a number of novel loci and probable causal genes in both lipid and glucose homeostasis have been newly discovered by the manual curation and clustering approaches in this study, including \",/*#__PURE__*/e(\"em\",{children:\"LDL-RAP1\"}),\"\u2019s association with lipoprotein and fatty acids, amino acid-associated \",/*#__PURE__*/e(\"em\",{children:\"SLC2A4RG\"}),\" and \",/*#__PURE__*/e(\"em\",{children:\"KCNK16 \"}),\"loci\"]}),/*#__PURE__*/e(\"p\",{children:\"This pioneering study highlights the value of associating data on genetics, metabolic traits, and disease outcomes at large scale to identify high-confidence causal relationship between genes and diseases. The summary statistics is publicly available through the NHGRI-EBI GWAS catalogue (GCST90301941\u2013GCST90302173), and thus providing a cornerstone to continuously unmask novel genes\u2019 associations with metabolic processes in diverse diseases.\"})]});export const richText6=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"The partnership between MGI and Sistemas Gen\\xf3micos is a significant advancement in genetic testing and sequencing. By combining MGI's DNBSEQ\u2122 technology with Sistemas Gen\\xf3micos' genetic analysis solutions, we are setting new standards for precision medicine, clinical research, and genetic diagnostics.\"})});export const richText7=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"strong\",{children:\"The Power of MGI's DNBSEQ\u2122 Technology\"})}),/*#__PURE__*/e(\"p\",{children:\"At the core of this partnership is MGI's DNBSEQ\u2122 technology, which powers the DNBSEQ-G400 benchtop sequencer.  DNBSEQ-G400 delivers high-throughput sequencing at a lower cost while maintaining unparalleled accuracy. The technology is crucial in providing precise and reliable data, reducing the risk of misdiagnosis, and enabling clinicians to make more confident patient care decisions.\"}),/*#__PURE__*/e(\"p\",{children:\"The DNBSEQ-G400 sequencer offers comprehensive genomic coverage, including all exonic regions of the genome, flanking regions, and mitochondrial DNA (mtDNA). This level of precision is particularly vital in diagnosing hereditary monogenic diseases across various medical fields, from neurology to cardiology. By offering low-cost, high-accuracy sequencing, the system makes genomic testing more accessible, allowing more patients to benefit from personalized treatments.\"}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"strong\",{children:\"Comprehensive Genetic Solutions from Sistemas Gen\\xf3micos\"})}),/*#__PURE__*/e(\"p\",{children:\"Sistemas Gen\\xf3micos enhances the collaboration by offering a complete genetic solution that covers the entire exome and mtDNA. HubExome Plus Panel\\xae - GeneSGKit\\xae (CE-IVD) provides comprehensive variant detection, including single nucleotide variants (SNVs), insertions and deletions (INDELs), and copy number variations (CNVs). The solution also supports virtual panel design, allowing clinicians to target specific disease areas like cardiology, neurology, and immunology. These tailored panels make the system highly flexible and customizable, catering to the unique needs of different patient populations and medical conditions.\"}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"strong\",{children:\"Streamlining the Genetic Testing Workflow\"})}),/*#__PURE__*/e(\"p\",{children:\"The collaboration between MGI and Sistemas Gen\\xf3micos doesn't stop at sequencing technology. It extends into bioinformatics, offering a streamlined workflow integrating library preparation, sequencing, and data analysis. This complete workflow is designed to be user-friendly and efficient, reducing processing time from 8 hours to just 1.5 hours when using MGI's automated library preparation systems, such as the MGISP-100 and MGISP-960.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,qMq9M2mIjWNloUIbh5Z2Zvxsrxs.png\",\"data-framer-height\":\"805\",\"data-framer-width\":\"1921\",height:\"402\",src:\"https://framerusercontent.com/images/qMq9M2mIjWNloUIbh5Z2Zvxsrxs.png\",srcSet:\"https://framerusercontent.com/images/qMq9M2mIjWNloUIbh5Z2Zvxsrxs.png?scale-down-to=512 512w,https://framerusercontent.com/images/qMq9M2mIjWNloUIbh5Z2Zvxsrxs.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/qMq9M2mIjWNloUIbh5Z2Zvxsrxs.png 1921w\",style:{aspectRatio:\"1921 / 805\"},width:\"960\"}),/*#__PURE__*/e(\"p\",{children:\"The GeneSystems\\xae platform, which is part of the workflow, is a cloud-based solution for secondary and tertiary analysis. 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With features like automatic variant classification according to ACMG guidelines, and extensive filtering options including virtual panels based on HPO terms, GeneSystems\\xae ensures that the data generated by the sequencer is translated into actionable insights.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,0wDvSqxUK9ZzAeuhEYa5j84IQ.png\",\"data-framer-height\":\"365\",\"data-framer-width\":\"1907\",height:\"182\",src:\"https://framerusercontent.com/images/0wDvSqxUK9ZzAeuhEYa5j84IQ.png\",srcSet:\"https://framerusercontent.com/images/0wDvSqxUK9ZzAeuhEYa5j84IQ.png?scale-down-to=512 512w,https://framerusercontent.com/images/0wDvSqxUK9ZzAeuhEYa5j84IQ.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/0wDvSqxUK9ZzAeuhEYa5j84IQ.png 1907w\",style:{aspectRatio:\"1907 / 365\"},width:\"953\"}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"strong\",{children:\"A CE-IVD Certified Workflow for In Vitro Diagnostics\"})}),/*#__PURE__*/e(\"p\",{children:\"The full genetic sequencing solution, from library preparation to data analysis, is CE-IVD certified and complies with the latest IVDR regulations. This ensures it meets the highest standards for diagnostic accuracy and reliability, guaranteeing both safety and effectiveness.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,486oLGxxaVq6aDAczQVpXpmjsM.png\",\"data-framer-height\":\"977\",\"data-framer-width\":\"1807\",height:\"488\",src:\"https://framerusercontent.com/images/486oLGxxaVq6aDAczQVpXpmjsM.png\",srcSet:\"https://framerusercontent.com/images/486oLGxxaVq6aDAczQVpXpmjsM.png?scale-down-to=512 512w,https://framerusercontent.com/images/486oLGxxaVq6aDAczQVpXpmjsM.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/486oLGxxaVq6aDAczQVpXpmjsM.png 1807w\",style:{aspectRatio:\"1807 / 977\"},width:\"903\"}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"strong\",{children:\"Empowering Precision Medicine\"})}),/*#__PURE__*/e(\"p\",{children:\"This partnership ultimately enables more precise diagnosis and personalized treatment plans. By combining MGI\u2019s cutting-edge sequencing technology with Sistemas Gen\\xf3micos' expertise in genetic analysis, the collaboration facilitates a deeper understanding of genetic disorders. For researchers, clinicians, and patients, this means more accurate diagnoses and the ability to tailor treatments to individual genetic profiles.\"}),/*#__PURE__*/e(\"p\",{children:\"With the combination of MGI's DNBSEQ\u2122 technology and Sistemas Gen\\xf3micos' comprehensive genetic solutions, the future of genetic testing looks more promising than ever. The collaboration exemplifies the synergy between cutting-edge technology and deep scientific expertise, helping to drive innovations in precision medicine and clinical research.\"}),/*#__PURE__*/e(\"p\",{children:\"To learn more about the workflow, please download our latest Application Note.\"})]});export const richText8=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Explore a comprehensive step-by-step guide to quality control (QC) in Next-Generation Sequencing (NGS) data analysis. Learn essential tools and methods, from FastQC to MultiQC, to ensure your NGS data is accurate and reliable for downstream analysis.\"})});export const richText9=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/t(\"p\",{children:[/*#__PURE__*/e(\"strong\",{children:\"\uD83D\uDD2C\"}),\"As someone who frequently gets asked about how to perform quality control (QC) on Next-Generation Sequencing (NGS) data, I wanted to share a detailed guide that outlines the essential steps. Proper QC is crucial for ensuring that your NGS data is accurate, reliable, and ready for downstream analysis. Whether you\u2019re new to NGS or just looking to refine your workflow, this guide will help you achieve the highest quality data.\"]}),/*#__PURE__*/t(\"ol\",{children:[/*#__PURE__*/e(\"li\",{\"data-preset-tag\":\"p\",children:/*#__PURE__*/t(\"p\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Assess Raw Data Quality\"}),\": \",/*#__PURE__*/e(\"strong\",{children:\"FastQC\"}),\": Begin by evaluating your raw sequencing reads with FastQC. This tool provides initial insights into base quality scores, GC content, and overrepresented sequences, helping you identify any immediate issues with your data.\"]})}),/*#__PURE__*/e(\"li\",{\"data-preset-tag\":\"p\",children:/*#__PURE__*/t(\"p\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Trim and Filter Reads\"}),\": \",/*#__PURE__*/e(\"strong\",{children:\"Adapter Removal\"}),\": Remove adapter sequences and low-quality bases using tools like Trimmomatic, Cutadapt, or SOAPnuke. The best trimming tool often depends on the sequencing platform used, so choose the one most appropriate for your data. \",/*#__PURE__*/e(\"strong\",{children:\"Quality Filtering\"}),\": Apply filters to remove reads with low Phred scores (e.g., below 20) to ensure that only high-quality reads are retained for further analysis.\"]})}),/*#__PURE__*/e(\"li\",{\"data-preset-tag\":\"p\",children:/*#__PURE__*/t(\"p\",{children:[/*#__PURE__*/e(\"strong\",{children:\"Evaluate Alignment Quality\"}),\": \",/*#__PURE__*/e(\"strong\",{children:\"Mapping Quality\"}),\": Align your reads to a reference genome using tools like BWA or Bowtie2. After alignment, evaluate the quality of your mapping with tools such as SAMtools, Picard, and Qualimap. 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Next Generation Sequencing (NGS) has become the new standard for genotypic drug resistance testing. Currently, MGI Tech Co. Ltd (MGI) offers a collection of DNBSEQTM genetic sequencing platforms to support NGS, providing users with comprehensive, flexible, and efficient sequencing options. MGI\u2019s Genetic Sequencer DNBSEQ-G400 (Fig. 1, MGI, Cat. No. 900-000168-00) high-throughput sequencing platform, utilizes an innovative flow cell system which can support various sequencing modes and an optimized optical and biochemical system that enables the whole sequencing process to be completed within a short period of time, offering the user a simplified and stream-lined sequencing experience. In this application note, we present the evaluation of the performance of MGI\u2019s high-throughput sequencing platform, DNBSEQ-G400, compared to two other instruments available on the market, for microbiological sample pooling sequencing using high-throughput sequencing (MGI, Universal Sequencing Reaction Kit \u2013 G400 SM FCL PE150, Cat. No. 1000022482*) with ABL\u2019s DeepChek\\xae assays and DeepChek\\xae software.\",/*#__PURE__*/e(\"br\",{}),/*#__PURE__*/e(\"br\",{}),/*#__PURE__*/e(\"br\",{className:\"trailing-break\"})]}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,istHudgFC12havRI1DTv84WvZAc.png\",\"data-framer-height\":\"549\",\"data-framer-width\":\"1267\",height:\"274\",src:\"https://framerusercontent.com/images/istHudgFC12havRI1DTv84WvZAc.png\",srcSet:\"https://framerusercontent.com/images/istHudgFC12havRI1DTv84WvZAc.png?scale-down-to=512 512w,https://framerusercontent.com/images/istHudgFC12havRI1DTv84WvZAc.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/istHudgFC12havRI1DTv84WvZAc.png 1267w\",style:{aspectRatio:\"1267 / 549\"},width:\"633\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"em\",{children:\"Fig. 1: MGI\u2019s DNBSEQ-G400 is built with a new flow cell system that can flexibly support a variety of different sequencing modes. It adopts optimized optical and biochemical systems, which can complete the sequencing process rapidly, providing users with a more streamlined sequencing experience.\"})})}),/*#__PURE__*/e(\"h3\",{children:\"Workflow Highlights \"}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"strong\",{children:\"PCR/RT-PCR and Library Preparation \"})}),/*#__PURE__*/e(\"p\",{children:\"To evaluate the microbiological samples pooling sequencing performance of MGI\u2019s Genetic Sequencer DNBSEQ-G400, positive leftover plasmas (HIV, HCV and HBV), positive leftover viral transport media (SARS-CoV-2), positive sputum control (Mycobacterium Tuberculosis) and HIV-1 external controls were purified using MagNa Pure24 (Roche). A total of 16 samples were tested: amplifications (Cat. No. ABL: 121A, 122A, 198B, 105A, 107D, 108A, 109A, 184A, 159C, 128A) and libraries (Cat. No. ABL: 116B and 124B) were performed using DeepChek\\xae assays (ABL) intended for target specific and whole genome sequencing and NGS library preparation. The NGS libraries were also converted using the MGIEasy Universal Library Conversion Kit (Cat. No. 1000004155). Libraries were sequenced (2x150bp) using the DNBSEQ-G400 and instrument platforms 1 and 2 for comparison. Output sequences were compared to the interest pathogen reference genomes (Fig. 2). \"}),/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"strong\",{children:\"MGI DNBSEQ\u2122 Sequencing\"})}),/*#__PURE__*/e(\"p\",{children:\"DNB sequencing takes place on high-density pattern arrays that immobilize individual DNBs for highly parallelized tracking of dNTP incorporation during strand extension. Sequencing starts with the hybridization of DNBs to anchor spots. After primer hybridization, the flow cell is flushed with fluorescently labeled dNTP probes. Unbound probes are washed away, and bound probes are stimulated to fluoresce. High resolution imaging and proprietary algorithms transform signals into high-quality and highly accurate sequencing results. Sequencing was performed according to the DNBSEQ-G400 protocol employing the PE150 mode. Based on DNBSEQTM technology (Fig. 3), the DNBSEQ-G400 and compatible DNBSEQ-G400 high throughput sequencing kits generate high quality and highly accurate sequencing results.\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,xdgFnm3uPhnJE3LNIJHkN7DaYAc.png\",\"data-framer-height\":\"593\",\"data-framer-width\":\"1224\",height:\"296\",src:\"https://framerusercontent.com/images/xdgFnm3uPhnJE3LNIJHkN7DaYAc.png\",srcSet:\"https://framerusercontent.com/images/xdgFnm3uPhnJE3LNIJHkN7DaYAc.png?scale-down-to=512 512w,https://framerusercontent.com/images/xdgFnm3uPhnJE3LNIJHkN7DaYAc.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/xdgFnm3uPhnJE3LNIJHkN7DaYAc.png 1224w\",style:{aspectRatio:\"1224 / 593\"},width:\"612\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"em\",{children:\"Fig. 2: The workflow of pooling sequencing with DeepChek\\xae assays and MGI sequencing platform. Example of a weekly NGS run on 24 samples.\"})})}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,xzpEAXCulyYvQtVjFqAVV8aFQA.png\",\"data-framer-height\":\"743\",\"data-framer-width\":\"1270\",height:\"371\",src:\"https://framerusercontent.com/images/xzpEAXCulyYvQtVjFqAVV8aFQA.png\",srcSet:\"https://framerusercontent.com/images/xzpEAXCulyYvQtVjFqAVV8aFQA.png?scale-down-to=512 512w,https://framerusercontent.com/images/xzpEAXCulyYvQtVjFqAVV8aFQA.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/xzpEAXCulyYvQtVjFqAVV8aFQA.png 1270w\",style:{aspectRatio:\"1270 / 743\"},width:\"635\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"em\",{children:\"Fig. 3: DNB sequencing uses the combinatory probe anchor system (cPAS) on a patterned array flow cell to provide high sequencing accuracy with improved imaging and reduced index hopping.\"})})}),/*#__PURE__*/e(\"h3\",{children:\"Data Analysis and sequencing assembly\"}),/*#__PURE__*/e(\"p\",{children:\"The DeepChek\\xae software (ABL, Luxembourg) was used for the analysis of subtypes, mutations and induced drug resistance for all pathogens. DeepChek\\xae (ABL, Luxembourg) is a CE marked downstream software capable of performing automated sequencing analysis from multiple inputs such as Sanger sequencing trace files (ABI), Fasta or FastQ file from NGS workflows (MGI sequencing data). The pipeline consists of 10 major steps which are: 1) Data cleaning, 2) Subtyping, 3) Tropism analysis (for HIV), 4) Alignment, 5) Post alignment cleaning, 6) Consensus creation, 7) Variant calling, 8) Expert system filtering, 9) Drug resistance calculation and 10) Reporting (Fig. 4). DeepChek\\xae is a secured web application which can be used through cloud access or locally, through pre-configured servers within the IT network of each laboratory. The cloud platform is available on dedicated servers labelled HDS (\u201CH\\xe9bergeur de Donn\\xe9es de Sant\\xe9\u201D/Health Data Hosting) which is a certification required for entities such as cloud service providers that host the personal health data governed by European and French laws and collected for delivering preventive, diagnostic, and other health services. Statistical analysis was performed using Prism 9 software (version 9.5.1).\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,LgMCpIOSNrGY8vApx07s4p255kU.png\",\"data-framer-height\":\"779\",\"data-framer-width\":\"1271\",height:\"389\",src:\"https://framerusercontent.com/images/LgMCpIOSNrGY8vApx07s4p255kU.png\",srcSet:\"https://framerusercontent.com/images/LgMCpIOSNrGY8vApx07s4p255kU.png?scale-down-to=512 512w,https://framerusercontent.com/images/LgMCpIOSNrGY8vApx07s4p255kU.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/LgMCpIOSNrGY8vApx07s4p255kU.png 1271w\",style:{aspectRatio:\"1271 / 779\"},width:\"635\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"em\",{children:\"Fig. 4: Data analysis workflow for ABL Diagnostics software systems. The DeepChek\\xae software system is a secured web application which can be used through cloud access or locally, through pre-configured servers. It is made available with regular updates (new clinical databases, guidelines\u2026) and quarterly upgrades (new features, modules, applications\u2026) and can be fully integrated within the IT network of each laboratory (integration with the sequencing platform, with the Laboratory Information System \u2013 LIS, with the Hospital Information System \u2013 HIS\u2026).\"})})}),/*#__PURE__*/e(\"h3\",{children:\"Results \"}),/*#__PURE__*/e(\"p\",{children:\"ABL Diagnostics MGI The Q30 score was 95%, and 75% and 86% for DNBSEQ-G400 instrument, and instrument 1 and 2 respectively. The median sequence number per sample was 190.040, and 768.506 and 102.488 for DNBSEQ-G400 instrument and instrument 1 and 2 respectively. Only 0.25% of the DNBSEQ-G400 instrument reads for each sample (randomly from each file and balanced over the 4 lanes) was used. All samples were accurately genotyped, and all mutations of interest were detected with the three NGS platforms: the DNBSEQ-G400 and instrument 1 and 2 (Fig. 5). Significant difference is observed for the percentage of reads mapped to the pathogen between DNBSEQ-G400/instrument 1 and DNBSEQ G400/instrument 2, (p < 0.01 p = 0.03), respectively (Fig. 6). No significant difference is observed with the total number of mutations of interest (Fig. 7 and Fig. 8).\"}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,WJbG6i6SN78zVFh9nzxWyhsqOak.png\",\"data-framer-height\":\"583\",\"data-framer-width\":\"1303\",height:\"291\",src:\"https://framerusercontent.com/images/WJbG6i6SN78zVFh9nzxWyhsqOak.png\",srcSet:\"https://framerusercontent.com/images/WJbG6i6SN78zVFh9nzxWyhsqOak.png?scale-down-to=512 512w,https://framerusercontent.com/images/WJbG6i6SN78zVFh9nzxWyhsqOak.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/WJbG6i6SN78zVFh9nzxWyhsqOak.png 1303w\",style:{aspectRatio:\"1303 / 583\"},width:\"651\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"em\",{children:\"Fig 5: Comparison of the HIV-1 subtype and drug resistance mutation using DeepChek\\xae software.\"})})}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,jfTAvJjiaR5QufqlIkhl7axA.png\",\"data-framer-height\":\"516\",\"data-framer-width\":\"1261\",height:\"258\",src:\"https://framerusercontent.com/images/jfTAvJjiaR5QufqlIkhl7axA.png\",srcSet:\"https://framerusercontent.com/images/jfTAvJjiaR5QufqlIkhl7axA.png?scale-down-to=512 512w,https://framerusercontent.com/images/jfTAvJjiaR5QufqlIkhl7axA.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/jfTAvJjiaR5QufqlIkhl7axA.png 1261w\",style:{aspectRatio:\"1261 / 516\"},width:\"630\"}),/*#__PURE__*/e(\"blockquote\",{children:/*#__PURE__*/e(\"p\",{children:/*#__PURE__*/e(\"em\",{children:\"Fig. 6: % of read mapped to the pathogen Fig. 7: TOTAL Minority Mutations: (>1% to <20%) Fig. 8: TOTAL Majority Mutations (>20% to <100%)\"})})}),/*#__PURE__*/e(\"h3\",{children:\"Conclusion \"}),/*#__PURE__*/e(\"p\",{children:\"Equivalent results between the DNBSEQ-G400 and instrument 1 and instrument 2 were observed for all pathogens. MGI data generation, MGI % of reads and MGI Q30 on the DNBSEQ-G400 were superior to instrument 1 and instrument 2. Next generation sequencing should occupy a major place in microbiology applications testing for subtyping, mutation determination and analysis, and drug resistance surveillance. It should enable to reveal resistant minority variants or new mutations and study their impact. The used sequencing methods show an overall comparable quality: further head-to-head comparisons shall be conducted to better determine the use-case of each platform, turn-around time, and economics. DeepChek\\xae assays using MGI DNBSEQTM NGS technology with an easy-to-use software, such as the DeepChek\\xae software, has the capacity to accommodate a greater number of samples than those presented in this study and can be used with the G99 and E25 (data not showed: data can be made available upon request).\"}),/*#__PURE__*/e(\"p\",{children:\"Disclaimer: The products listed in this document are to be used by trained personnel only. All products are to be used only in accordance with local laws, regulations, and package instructions. For Research Use Only (RUO): not for use in diagnostic procedures, no claim or representation is intended to provide information for the diagnosis, prevention, or treatment of disease. Please contact our support team to request the appropriate Instructions for Use (IFU) and registration status of the above-mentioned products for your respective territories. DeepChek\\xae is a registered trademark of Advanced Biological Laboratories (ABL) SA. All other product names, trademarks, and logos are the property of their respective owners. \"}),/*#__PURE__*/e(\"p\",{children:\"*Unless otherwise informed, this StandardMPS sequencing reagent is not available in Germany, UK, Sweden, and Switzerland\"}),/*#__PURE__*/e(\"h3\",{children:\"About MGI \"}),/*#__PURE__*/e(\"p\",{children:\"AMGI MGI Tech Co., Ltd. (referred to as \u201CMGI\u201D) is committed to building core tools and technologies to lead life science through intelligent innovation. MGI focuses on R&D, production and sales of DNA sequencing instruments, reagents, and related products to support life science research, agriculture, precision medicine and healthcare. MGI is a leading producer of clinical high-throughput gene sequencers, and its multi-omics platforms include genetic sequencing, medical imaging, and laboratory automation. MGI, founded in 2016 in Shenzhen, China, is one of the few companies worldwide that can develop and mass-produce clinical genetic sequencers. Providing real-time, comprehensive, life-long solutions, its vision is to lead life science innovation. At present, MGI has more than 1,800 employees and operates in more than 70 countries and regions Wseurvhianng, more than 1,100 customers around the world, with 8 international subsidiaries including Qingdao, Changchun, Kunshan and Hongkong, China, Japan, UAE, Latvia, and USA. \"}),/*#__PURE__*/e(\"h3\",{children:\"About ABL Diagnostics \"}),/*#__PURE__*/e(\"p\",{children:\"ABL DIAGNOSTICS S.A. (ABLD) is a worldwide leading international company offering innovative and proprietary molecular biology assays and end-to-end solutions intended to be used for molecular detection by Polymerase Chain Reaction (PCR) \u2013 UltraGene\\xae and for genotyping through DNA sequencing \u2013 DeepChek\\xae. These molecular biology products cover one of the largest portfolio of microbiology applications, growing fast year after year to stick to the market needs, with a primary focus on HIV (with target-specific assays covering all relevant genes used for drug resistance assessment like reverse transcriptase, protease, integrase and with the disruptive Whole Genome Kit), on SARS-CoV-2, on Tuberculosis (with a multiplex assay targeting genes relevant for first line, second line and new-drugs resistance determination), on viral hepatitis B and C, 16s/18s RNA for taxonomy and microbiome analyses and other viral and bacterial targets. ABL Diagnostics also develops, manufactures, and markets kits for clinical specimen collection \u2013 MediaChek\\xae and digital solutions like Nadis\\xae, an Electronic Medical Record (EMR) system used in France in more than 200 hospitals managing patients infected by HIV or Viral Hepatitis. ABL Diagnostics, based in Woippy, is a public company listed in compartment B of Euronext\u2019s regulated market in Paris (Euronext: ABLD \u2013 ISIN: FR001400AHX6). \"}),/*#__PURE__*/t(\"p\",{children:[\"For further information, please visit \",/*#__PURE__*/e(n,{href:\"https://www.abldiagnostics.com/\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"www.abldiagnostics.com\"})}),\".\",/*#__PURE__*/e(\"br\",{}),/*#__PURE__*/e(\"br\",{className:\"trailing-break\"})]}),/*#__PURE__*/t(\"h4\",{children:[/*#__PURE__*/e(\"br\",{}),\"Authors\"]}),/*#__PURE__*/e(\"p\",{children:\"Laura Pellegrino, Sofiane Mohamed, Ludwig Eurlings, Chalom Sayada, Dimitri Gonzalez, Bret Wurdeman, Ronan Boulm\\xe9\"})]});export const richText12=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Revolutionizing single-cell RNA-seq, SMART-seq3Xpress offers unparalleled sensitivity and cost-efficiency with full-length coverage.\"})});export const richText13=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/e(\"p\",{children:\"Single-cell sequencing, which provides a high-resolution of cellular differences by sequencing nucleic acid information namely DNA and RNA from individual cells, has become one of the key applications in the next-generation sequencing (NGS) to better understand the individual heterogeneity in physiological processes and the personal differences in responding of medical treatments, shedding light on discovering novel biological mechanisms and personalized therapies.\"}),/*#__PURE__*/t(\"p\",{children:[\"Currently, most single-cell RNA-sequencing (scRNA-seq) methods map a short part of the RNA molecules, from either the 5\u2032 or 3\u2032 end, together with a unique molecular identifier (UMI)(Mereu et al., \",/*#__PURE__*/e(\"em\",{children:\"Nat. Biotechnol\"}),\", 2020). These RNA end-counting strategies have been efficient in estimating gene expression across large numbers of cells, while controlling for PCR amplification biases attributed to low quantity input of RNAs and compensating on the throughput capacity of sequencing, but the limited coverage of those methods heavily obscure the detection of those transcribed genetic variations and transcript isoform expression caused by, for instance, RNA alternative-splicing, which is a common mechanism of RNA processing and abnormalities in such a process have been shown to associate with the development of various diseases, such as cancers and neurodegenerative diseases (Zhang et al., \",/*#__PURE__*/e(\"em\",{children:\"Nature\"}),\", 2021; Garcia-Blanco et al., \",/*#__PURE__*/e(\"em\",{children:\"Nat. Biotechn\"}),\"ol, 2004; Mills et al., \",/*#__PURE__*/e(\"em\",{children:\"Neurobiol Aging\"}),\", 2012).\\xa0\\xa0 Moreover, though those end-counting scRNA-seq enable higher throughput sequencing capacity in terms of cell number, their relatively low sensitivity and high cost are the major bottlenecks to be challenged. In the meanwhile, long-read sequencing technologies enable direct quantification of allele- and isoform-level expression, however their current read depths, biased-error rate and sample preparation procedures obstruct their broad applications across cells, tissues and organisms (Byrne et al., \",/*#__PURE__*/e(\"em\",{children:\"Nat. Commum\"}),\", 2017; Gupta et al., \",/*#__PURE__*/e(\"em\",{children:\"Nat. Biotechnol\"}),\", 2018)\"]}),/*#__PURE__*/t(\"p\",{children:[\"To overcome those technical demerits, Hagemann-Jensen and Ziegenhain et al. at Karolinska Institute (\",/*#__PURE__*/e(\"em\",{children:\"Nat. Biotechnol\"}),\", 2022; \",/*#__PURE__*/e(\"em\",{children:\"Nat. Biotechnol, 2020. https://www.nature.com/articles/s41587-022-01311-4\"}),\") have developed and further optimized a scalable and sensitive short-read based sequencing method, Smart-seq3xpress to enable full-length RNA coverage and more importantly to provide an unprecedent high-resolution of individual RNAs to isoforms and their alleic origin in single cells with lower operating cost and shortened library preparation duration to a single workday. The well-established Smart-seq3xpress protocol implements UMI in the 5\u2019-end of full-length RNA transcripts to establish a unique identity of each RNA molecule, thus inclusion of UMI empowers the detection of transcript copy number variation (CNV) and to overcome the biases caused by PCR amplification processes, further bringing up sequencing sensitivity and specificity. Moreover, Smart-seq3xpress is a plate-based method, which could be easily scaled up from 96- to 384-well plates possibly facilitated by automation processes. More practically, sorting of the cells of interest into the well-plates can be separated in spatial and temporal manner as the filled well-plates can be frozen anytime until further processing, showing a major advantage when compared to the method that viable cell suspension needs to be processed immediately. Moreover, this study also put substantial effort in miniaturizing the key factors in scRNA-seq, including lowering the reaction volume, decreasing the input of cDNA, reducing PCR cycles, and adjusting Tn5 and cDNA ratio for more cost-effective tagmentation without compromising the detection performance. Additionally, adjusting of the salt components and concentration required for the reactions, implementation of new enzymes and uniquely setting an extensive RNA count QC by utilizing of self-developed new UMIcountR R package to further improve the overall detection power of Smart-seq3xpress. Furthermore, this method provides a more environmental-friendly and cost-effective solution because the material and resources are ten-fold less when compared with other RNA-seq methods, and the usage plastics consumables is significantly reduced by new\\xa03D-printed tools and by contact-less reagent dispensing as well as pre-dispensed desiccated index primers`.\"]})]});export const richText14=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/e(\"h6\",{children:/*#__PURE__*/e(\"em\",{children:\"fig. 1 | Scalable full-transcript coverage scRNA-seq with Smart-seq3xpress. a, Schematic of nanoliter cDNA synthesis reactions performed in wells of 384-well PCR plates with 3\u2009\\xb5l of hydrophobic overlay. b, Illustration of reduced-volume experiments with the lysis, RT and PCR volumes used. c, The number of genes detected per HEK293TF cell at each reaction volume, when sampling 100,000 sequencing reads (n=\u2009100, 19, 32 and 28 cells, respectively). P value represents a two-sided t-test between the 10-\\xb5l and 1-\\xb5l conditions. d, Influence of hydrophobic overlays on miniaturized cDNA synthesis (1\u2009\\xb5l total volume). For each compound, boxes depict the number of genes detected per HEK293FT cell (n=\u200917, 34, 39, 28, 25, 24, 28, 38 and 70, respectively), subsampled at 200,000 sequencing reads per cell. e, Replacement of the bead-based cDNA cleanup by dilution in single HEK293FT (n=\u200958 and 52, respectively) cells. Box plots show the number of genes detected per cell and condition (at 100,000 reads) with P value for a two-sided t-test across conditions. f, Tagmentation complexity using 0.1\u2009\\xb5l of ATM Tn5 enzyme per HEK293FT cell in relation to input cDNA. The median number of detected genes as a function of raw sequencing reads (n=\u200951, 53, 54, 53, 53 and 52 cells for 25, 50, 75, 100, 200 and 500\u2009pg, respectively). g, Tagmentation complexity for varying amounts of cDNA input. Complexity was summarized as unique aligned and gene-assigned UMI-containing read pairs per 400,000 raw reads and HEK293FT cell (n=\u200949, 51, 51, 50, 51 and 44). h, Schematic outline of the Smart-seq3 and Smartseq3xpress workflows. i, The number of genes detected with Smart-seq3xpress after variable amounts of pre-amplification PCR cycles. Median number of genes is reported as a function of raw sequencing reads in HEK293FT cells (n=\u200993, 98, 108, 113, 102, 114 and 118 cells for 10, 12, 13, 14, 15, 16 or 20 cycles, respectively). j, Fraction of UMI-containing reads to internal reads for HEK293FT cells prepared with Smartseq3xpress (KAPA HiFi; 12 PCR cycles), at a variable range of TDE1 Tn5 amounts (n=\u200964 cells each). k, Fraction of UMI-containing reads to internal reads for HEK293FT cells prepared with Smartseq3xpress (SeqAmp; 12 PCR cycles), at a variable range of TDE1 Tn5 amounts (n=\u200960 cells each). l,m, Optimization of RT and PCR conditions across 376 experimental conditions on HEK293FT cells. Colors indicate particular experimental conditions: Smart-seq3xpress with Smart-seq3 TSO (purple; n=\u2009912), 52\u2009\\xb0C RT/alternate TSO implementation (yellow; n=\u200974), fixed spacer TSO variant (blue; n=\u200945), FLASH-seq TSO variant (green; n=\u200955), Smart-seq3xpress with improved TSO (pink; n=\u200963) and all other conditions (gray; n=\u200921,707). Scatter plots denote the level of artifactual TSO-UMI reads and RNA counting errors (l) as well as a percentage of ribosomal RNA (rRNA) mapped reads and number of detected genes in 100,000 reads after removal of strand invasion reads (m). n, Benchmarking of Smart-seq3 variants. Box plots show the number of genes detected per HEK293FT cell in full-volume Smart-seq3 (ref. 2), low-volume Smart-seq3 and Smart-seq3xpress implementations, at the indicated read depths (n=\u2009109\u2013110, 18\u201327, 9\u2013170, 20\u201355 and 9\u201363 cells, depending on the cells available at the given sequencing depths). The box plots (in c, d, e, j, k and n) show the median and first and third quartiles as a box, and the whiskers\\xa0indicate the most extreme data points within 1.5 lengths of the box. cSt, centistoke.\"})})});export const richText15=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/t(\"p\",{children:[\"To demonstrate Smart-seq3xpress, Hagemann-Jensen and Ziegenhain et al. examined 26,260 human peripheral blood mononuclear cells (hPBMCs) with an average depth of 258,000 read pairs per cell. The results provided a full-length transcript profiling and showed the reconstruction of T-cell receptor sequences, identifying several cell types and states, including unconventional T-cell populations such as MAIT, and gamma-delta T-cells, with higher gene detection across cell types. Notably, these results are achieved by using almost 10 times less hPBMCs when compared to the droplet-based sc-RNAseq method. In the following comparison study with a droplet-based method, Smart-seq3xpress gave more superior performances in multiple aspects in terms of single-nucleotide polymorphism (SNP) detection (9-fold higher), more read support over exon\u2013exon/exon\u2013intron splice junctions by its full-length RNA coverage and T-cell clustering consistency. The study of hPBMC Smart-seq3xpress data on alternative splicing further revealed significant variation in inclusion levels and distinct splicing patterns among cell types, of which are independent from their gene expression level. A later benchmarking study (Probst et al., \",/*#__PURE__*/e(\"em\",{children:\"BMC Genomics\"}),\", 2022) on full-length scRNAseq indicated that Smart-seq3 protocol presented the highest gene detection per single cell at the lower price when compared with other kits.\",/*#__PURE__*/e(\"br\",{}),/*#__PURE__*/e(\"br\",{}),\"About MGI\"]}),/*#__PURE__*/t(\"p\",{children:[\"MGI Tech Co., Ltd. (MGI) is at the forefront of global innovation, actively contributing to life science through intelligent innovation. With a presence in over 100 countries and a customer base of 2600+, MGI's cutting-edge technology has been instrumental in the development of 736+ user patents, facilitating the creation of over 150 petabytes of data. The company's extensive portfolio includes \",/*#__PURE__*/e(n,{href:{webPageId:\"eZTQUyXy4\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"sequencing instruments\"})}),\", \",/*#__PURE__*/e(n,{href:{webPageId:\"NWP4c4LYR\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"automation instruments\"})}),\", reagents, and related products that cater to various sectors such as life science research, agriculture, precision medicine, and healthcare.\"]}),/*#__PURE__*/e(\"p\",{children:\"MGI is dedicated to advancing life science tools for the healthcare of the future. As of December 2021, the company's global presence has expanded to more than 100 countries and regions, serving over 1,300 international clients. With a workforce of over 2,900 professionals worldwide, including 5 centers for research and development and production facilities in Europe, MGI is committed to fostering innovation. Approximately 35% of MGI's employees are engaged in R&D, underscoring the company's focus on pioneering advancements in the field.\"}),/*#__PURE__*/e(\"p\",{children:\"The impact of MGI's work is further evidenced by the publication of over 6,800 papers in prestigious scientific journals, showcasing the significant contribution of MGI's technology to the scientific community and beyond.\"}),/*#__PURE__*/t(\"p\",{children:[\"For more information about MGI and its contributions to life science and healthcare, please visit the \",/*#__PURE__*/e(n,{href:{webPageId:\"augiA20Il\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"MGI website\"})}),\" or connect with us on \",/*#__PURE__*/e(n,{href:\"https://twitter.com/MGI_Technology\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"Twitter\"})}),\", \",/*#__PURE__*/e(n,{href:\"https://www.linkedin.com/company/mgi-tech688114/mycompany/\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"LinkedIn\"})}),\", or \",/*#__PURE__*/e(n,{href:\"https://www.youtube.com/@MGIInternational\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"YouTube\"})}),\".\"]})]});export const richText16=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Explore how sc-StereoSeq is transforming our understanding of plant biology, from crop production to plant stress responses, marking a new era in spatial transcriptomics.\"})});export const richText17=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/e(\"h3\",{children:\"Unveiling the New Era in Plant Biology: Single-Cell Spatial Transcriptomics (sc-StereoSeq)\\xa0\"}),/*#__PURE__*/e(\"p\",{children:\"Single-cell Spatial transcriptomics (sc-StereoSeq) is opening a new era in plant and crop biology.\\xa0\"}),/*#__PURE__*/e(\"h3\",{children:\"Bridging Spatial Information and Gene Expression in Plant Tissue Cells, advances and challenges\\xa0\\xa0\"}),/*#__PURE__*/e(\"p\",{children:\"Associating spatial information with gene expression at high resolution in plant tissue cells is essential to further understanding of plant physiological processes, such as development, evolution, and interactions of plants with (a)biotic stresses. The obtained new knowledge could lead to substantial significance in benefiting crop production and resistance, eventually alleviating the food shortage issue for a better life and society.\\xa0\"}),/*#__PURE__*/e(\"p\",{children:\"In the last years, the fast pace in spatial transcriptomic (ST) technologies has overcome previous technical constraints in throughput capacity, resolution, and lack of cellular heterogeneity content, fine-tuning the functions of specific cell groups with their spatial details.The power of ST has led to discoveries in multiple fields, including embryonic development, neuroscience, and cancer research (Chen et al., Cell, 2022; Wei et al., Science, 2022; Wu et al., Nature 2021). However, its application to plant studies has fallen behind when compared with human and animal research due to several remaining challenges inherent to the nature of plants.First, the presence of cell wall hinders the plant morphological staining, tissue section embedding, and subsequent permeabilization for RNA capture. Protoplast method may trigger artificial changes, resulting in global gene expression alteration, biased proportions of cell types, thus affecting the final transcriptome profiling. Second, the high-water content in plants could cause difficulty in preserving the original spatial positions due to a common diffusion phenomenon especially when using cryo-sections. Third, it is known that certain secondary metabolites in plant tissues suppress RNA capture efficiency, causing bias in RNA molecule diversity and yield.\\xa0\"})]});export const richText18=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/e(\"h3\",{children:\"A Path-Breaking Study on Arabidopsis Leaves, unrevealing subtle but significant transcriptional differences between cell\\xa0subtypes\\xa0\"}),/*#__PURE__*/e(\"p\",{children:\"\\xa0In spite of these bottlenecks in plant spatial transcriptomics, a path-breaking study on Arabidopsis leaves published in Developmental Cells by a group of Chinese scientists (Xia, et al., 2022) presents comprehensive data and unprecedented sub-cellular resolutions in nm scale (500 nm) on gene expression coupled with specific cell-type and accurate spatial information, achieved by single-cell spatial transcriptomic sequencing (sc-StereoSeq) combined with MGI proprietary DNA nanoball technology (DNB). In this study, Xia et al. established and validated this bona fide sc-StereoSeq technique in cryo-sectioned Arabidopsis leaves, not only showing its consistency with previously known transcriptomic profiles and canonical marker genes but also enhancing the expressional and spatial resolution to unmask subtle yet significant transcriptional differences between cell subtypes. More than 10,000 cells from Arabidopsis cauline leaves with a total of 19,720 genes were detected with high quality and spatially resolved single-cell transcriptome profiles, showing superior performance when compared with the Bin20 method with lower resolution as the control in the study. Moreover, the distribution of molecular identifiers (MID) seeded on the chip demonstrated perfect alignment between leaf areas and transcript signals, showing no signal diffusion, which is commonly observed in other ST techniques. The epidermis in Arabidopsis leaves can be subdivided into upper and lower epidermal cells, and mesophyll cells can also be classified into spongy mesophyll cells and palisade mesophyll cells. Previously, scRNA-seq data alone were not able to distinguish those cell subtypes owing to their highly identical cell lineage and transcriptomic profiles.\\xa0\"}),/*#__PURE__*/e(\"p\",{children:\"With the excellent sub-cellular morphological information preserved by sc-StereoSeq, those cell subtypes and their boundaries could be well recognized. More importantly, cell-type-specific clusters of genes, which were not well reviewed in those cell subtypes, were identified, showing the genes associated with photosynthesis, cuticle development, and fatty acid biosynthesis were significantly up-regulated in upper epidermal cells, while the genes involved in immune response, response to absence of light, and cell death regulation were enriched in lower epidermal cells (Figure 3E). In addition, WAX2, which plays metabolic roles in both cuticle membrane and wax synthesis, and DIN6 involved in nitrogen metabolism are differentially expressed by upper- and lower-epidermal cells. These new findings revealed the subtle transcriptional differences between those cell subtypes, inspiring their unique yet to be investigated roles in plant physiology and functions. By taking advantage of the spatial content provided in this study, the expression analysis along the medio-lateral axis of the Arabidopsis leaf was also conducted to investigate any enriched specific gene cluster in certain areas of leaves. These spatial gene expression patterns along the medio-lateral leaf axis revealed by sc-StereoSeq provide indications to further understand the complex physiological functions and zonation in plant leaves. Lastly, the sensitivity of detecting subtle changes in gene expression between different cell types is significantly reinforced by sc-StereoSeq with its superior spatial information when compared with other conventional molecular methods.\"})]});export const richText19=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/e(\"h3\",{children:\"Future prospective of\\xa0sc-StereoSeq in plant and crop research\\xa0\"}),/*#__PURE__*/e(\"p\",{children:\"Following the light of this inspiring work, other similar studies applying or combining spatial transcriptomic assays have been done recently on portulaca leaf cells (Moreno-Villena et al., Sci Adv, 2022), peanut needle (Liu et al, Plant Biotechno J, 2022), orchid meristem (Liu et al, Nucleic Acids Res, 2022) and poplar vascular issue (Du et al., Mol plant, 2023). The next worth-trying study would be applying this established scStereo-seq methodology on\\xa0Arabidopsis/plant\\xa0root to gain more detailed, unbiased and higher-resolution on gene expression atlas to deepen the understanding and further extrapolate the key mechanisms involved in plant root development, of which process is as equally essential as the leaf development. Moreover, this molecular tool could further dissect how plant root responds to those environmental factors, such as nutrients, abiotic stresses, and microbial interactions, such as symbiosis processes and pathogen infections, and how the physiological adjustments are archived via the transcriptional regulations. Furthermore, collecting the tissues from the different developmental stages followed by scStereo-seq assay would significantly enrich the biological information at both spatial and temporal aspects, thus lifting the plant and crops research to a new horizon. Additionally, what could be possibly improved in this and similar studies in the future, especially when studying particular groups of cells, is to utilize the power of fluorescence reporter (GFP/mCherry) driven by cell type-specific promoters, such as pPIN2, pSCR, pSHR and pWOX5\\xa0in\\xa0Arabidopsis\\xa0root\\xa0(Marqu\\xe8s-Bueno\\xa0et al.,\\xa0Plant J, 2016),\\xa0pGC1 and pCER5 in leaf (Yang, et al., Plant methods, 2008;\\xa0Mustroph et al., PNAS, 2009),\\xa0to enable more precise and efficient cell identity classification besides the conventional morphology-based method. Cell boundary definition by staining and high-throughput fluorescence imaging to label per cell type before RNA extraction would further empower the potency and accuracy of scStereo-seq.\\xa0\"}),/*#__PURE__*/e(\"h3\",{children:\"About MGI\"}),/*#__PURE__*/t(\"p\",{children:[\"MGI Tech Co., Ltd. (MGI) is at the forefront of global innovation, actively contributing to life science through intelligent innovation. With a presence in over 100 countries and a customer base of 2600+, MGI's cutting-edge technology has been instrumental in the development of 736+ user patents, facilitating the creation of over 150 petabytes of data. The company's extensive portfolio includes \",/*#__PURE__*/e(n,{href:{webPageId:\"eZTQUyXy4\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"sequencing instruments\"})}),\", \",/*#__PURE__*/e(n,{href:{webPageId:\"NWP4c4LYR\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"automation instruments\"})}),\", reagents, and related products that cater to various sectors such as life science research, agriculture, precision medicine, and healthcare.\"]}),/*#__PURE__*/e(\"p\",{children:\"MGI is dedicated to advancing life science tools for the healthcare of the future. As of December 2021, the company's global presence has expanded to more than 100 countries and regions, serving over 1,300 international clients. With a workforce of over 2,900 professionals worldwide, including 5 centers for research and development and production facilities in Europe, MGI is committed to fostering innovation. Approximately 35% of MGI's employees are engaged in R&D, underscoring the company's focus on pioneering advancements in the field.\"}),/*#__PURE__*/e(\"p\",{children:\"The impact of MGI's work is further evidenced by the publication of over 6,800 papers in prestigious scientific journals, showcasing the significant contribution of MGI's technology to the scientific community and beyond.\"}),/*#__PURE__*/t(\"p\",{children:[\"For more information about MGI and its contributions to life science and healthcare, please visit the \",/*#__PURE__*/e(n,{href:{webPageId:\"augiA20Il\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"MGI website\"})}),\" or connect with us on \",/*#__PURE__*/e(n,{href:\"https://twitter.com/MGI_Technology\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"Twitter\"})}),\", \",/*#__PURE__*/e(n,{href:\"https://www.linkedin.com/company/mgi-tech688114/mycompany/\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"LinkedIn\"})}),\", or \",/*#__PURE__*/e(n,{href:\"https://www.youtube.com/@MGIInternational\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"YouTube\"})}),\".\"]})]});export const richText20=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Explore the transformative impact of Next-Generation Sequencing in mRNA vaccine development. Dive into Nobel-winning discoveries, challenges, and novel technologies like the VAX-seq workflow, enhancing vaccine production, safety, and quality analysis amidst global health crises.\"})});export const richText21=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Katalin Karik\\xf3 and Drew Weissman have been granted the 2023 Nobel prize in Physiology or Medicine for their subtle but significant discoveries concerning nucleoside base modifications (Fig1. ), namely replacement of uridine by N1-methylpseudouridine (m1\u03A8), which serves as the cornerstone to make mRNA delivery non-immunogenic and to enable the development of mRNA vaccines against COVID-19 with the unprecedented effectiveness and speed, saving millions of lives and ending not only the pandemic of a disease but also the catastrophic health-care burden globally.\"})});export const richText22=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,ytMxTLLrO1iRmnGamyUGvkdt5I.png\",\"data-framer-height\":\"684\",\"data-framer-width\":\"1216\",height:\"342\",src:\"https://framerusercontent.com/images/ytMxTLLrO1iRmnGamyUGvkdt5I.png\",srcSet:\"https://framerusercontent.com/images/ytMxTLLrO1iRmnGamyUGvkdt5I.png?scale-down-to=512 512w,https://framerusercontent.com/images/ytMxTLLrO1iRmnGamyUGvkdt5I.png?scale-down-to=1024 1024w,https://framerusercontent.com/images/ytMxTLLrO1iRmnGamyUGvkdt5I.png 1216w\",style:{aspectRatio:\"1216 / 684\"},width:\"608\"}),/*#__PURE__*/t(\"p\",{children:[\"Currently, many new mRNA-based therapies have been vigorously developed for a wide variety of diseases, including other infectious pathogens, cancer and auto-immune diseases. In the meanwhile, the concerns about its safety and potential side-effects have been undoubtfully arisen as happened to other new treatments. Though diverse techniques, such as RT-qPCR, capillary gel electrophoresis, IP-RP-HPLC and immunoblotting have been implemented in the quality analysis of mRNA vaccines, those methods are tedious, expensive and lack of sensitivity in detection essential mRNA features. Consequently, a demand on a robust and combinatory analytical method is urgently needed to evaluate mRNA vaccines integrity, efficiency, and more importantly to monitor contaminants and off-target effect to address those safety and specificity concerns. In the last few years, Next-generation sequencing technology has been advanced with a rapid pace on multiple aspects, including improved sequencing throughput capacity, speed, accuracy, significant reduction of cost and long-read development, promoting its various applications in both research and clinical diagnosis. Can next-generation sequencing assist the development and validation of mRNA vaccines and therapies?? \",/*#__PURE__*/e(\"br\",{}),/*#__PURE__*/e(\"br\",{}),/*#__PURE__*/e(\"br\",{}),\"An RNA-sequencing study published recently in Nature communication combined and evaluated both short- and long-read sequencing for mRNA vaccine and therapies analysis. The study proposed a streamlined method, VAX-seq workflow (Gunter et al., 2023) followed by an integrated bioinformatic tool called Mana to gain a comprehensive assessment on those mRNA key attributes, including sequence integrity, 3\u2019-end poly(A) tail length and DNA/RNA contaminants (Fig. 2). Moreover, VAX-seq also provides a solution in the detection of off-target RNAs, which are generated during in vitro transcription as truncated, readthrough or anti-sense form of RNAs triggering innate immune response. Both long- and short-read sequencing revealed the similar on- and off-target RNAs percentage.\"]})]});export const richText23=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/e(\"h3\",{children:\"About MGI\"}),/*#__PURE__*/t(\"p\",{children:[\"MGI Tech Co., Ltd. (MGI) is at the forefront of global innovation, actively contributing to life science through intelligent innovation. With a presence in over 100 countries and a customer base of 2600+, MGI's cutting-edge technology has been instrumental in the development of 736+ user patents, facilitating the creation of over 150 petabytes of data. The company's extensive portfolio includes \",/*#__PURE__*/e(n,{href:{webPageId:\"eZTQUyXy4\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"sequencing instruments\"})}),\", \",/*#__PURE__*/e(n,{href:{webPageId:\"NWP4c4LYR\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"automation instruments\"})}),\", reagents, and related products that cater to various sectors such as life science research, agriculture, precision medicine, and healthcare.\"]}),/*#__PURE__*/e(\"p\",{children:\"MGI is dedicated to advancing life science tools for the healthcare of the future. As of December 2021, the company's global presence has expanded to more than 100 countries and regions, serving over 1,300 international clients. With a workforce of over 2,900 professionals worldwide, including 5 centers for research and development and production facilities in Europe, MGI is committed to fostering innovation. Approximately 35% of MGI's employees are engaged in R&D, underscoring the company's focus on pioneering advancements in the field.\"}),/*#__PURE__*/e(\"p\",{children:\"The impact of MGI's work is further evidenced by the publication of over 6,800 papers in prestigious scientific journals, showcasing the significant contribution of MGI's technology to the scientific community and beyond.\"}),/*#__PURE__*/t(\"p\",{children:[\"For more information about MGI and its contributions to life science and healthcare, please visit the \",/*#__PURE__*/e(n,{href:{webPageId:\"augiA20Il\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"MGI website\"})}),\" or connect with us on \",/*#__PURE__*/e(n,{href:\"https://twitter.com/MGI_Technology\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"Twitter\"})}),\", \",/*#__PURE__*/e(n,{href:\"https://www.linkedin.com/company/mgi-tech688114/mycompany/\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"LinkedIn\"})}),\", or \",/*#__PURE__*/e(n,{href:\"https://www.youtube.com/@MGIInternational\",nodeId:\"iWpv9NMzk\",openInNewTab:!0,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"YouTube\"})}),\".\"]})]});export const richText24=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/e(\"p\",{children:\"Unlock the mysteries of the human Y chromosome with MGI's cutting-edge findings. Learn how the T2T consortium's study corrects GRCh38 errors, reveals 30Mb+ new sequence data, and advances our understanding of gene structures and their implications in the Omic-verse.\"})});export const richText25=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/t(\"p\",{children:['Though the human Y chromosome is a \"tiny\" guy among other chromosomes, it has been regarded as the notoriously difficult target to sequence and assemble because of its complex repeat structures including tandem repeats, long palindromes, and segmental duplications. Thus, not surprisingly, more than half of the Y chromosome has been missing from the GRCh38 reference and it remains the last riddle in human chromosome to be challenged.',/*#__PURE__*/e(\"br\",{}),/*#__PURE__*/e(\"br\",{className:\"trailing-break\"})]}),/*#__PURE__*/e(\"img\",{alt:\"\",className:\"framer-image\",\"data-framer-asset\":\"data:framer/asset-reference,pg9FPCKpuQcBwI7YgrbQ52NVZzk.svg\",\"data-framer-height\":\"5334\",\"data-framer-width\":\"8000\",height:\"2667\",src:\"https://framerusercontent.com/images/pg9FPCKpuQcBwI7YgrbQ52NVZzk.svg\",srcSet:\"https://framerusercontent.com/images/pg9FPCKpuQcBwI7YgrbQ52NVZzk.svg?scale-down-to=512 512w,https://framerusercontent.com/images/pg9FPCKpuQcBwI7YgrbQ52NVZzk.svg?scale-down-to=1024 1024w,https://framerusercontent.com/images/pg9FPCKpuQcBwI7YgrbQ52NVZzk.svg?scale-down-to=2048 2048w,https://framerusercontent.com/images/pg9FPCKpuQcBwI7YgrbQ52NVZzk.svg?scale-down-to=4096 4096w,https://framerusercontent.com/images/pg9FPCKpuQcBwI7YgrbQ52NVZzk.svg 8000w\",style:{aspectRatio:\"8000 / 5334\"},width:\"4000\"})]});export const richText26=/*#__PURE__*/e(i.Fragment,{children:/*#__PURE__*/t(\"p\",{children:[\"A milestone study published recently in Nature by the Telomere-to-Telomere (T2T) consortium (\",/*#__PURE__*/e(\"em\",{children:\"Rhie A et al., Nature, 2023\"}),\") presents the complete 62,460,029 base pair sequence of a human Y chromosome from the HG002 genome (T2T-Y) that corrects multiple errors in GRCh38-Y references and adds over 30 Mb of sequence, which are predominantly from the heterochromatic region of the Yqh-arm (Figure 1), unmasking the complete ampliconic structures of TSPY, DAZ, and RBMY, and moreover discovering 110\u2009genes, among which 42 are predicted to be protein-coding genes from the TSPY gene family. It has been known that 9 protein-coding ampliconic gene families have expanded specifically on the Y and are expressed in testis functioning in spermatogenesis and fertility. These findings are particularly intriguing when concerning of evolution and sex determination since Y chromosome has been lost by certain non-mammalian species and this latest study could inspire us to understand how mammals follows a similar biological fate.\"]})});export const richText27=/*#__PURE__*/t(i.Fragment,{children:[/*#__PURE__*/e(\"p\",{children:\"By combining T2T-Y with a prior assembly of the CHM13 genome, available population variation, clinical variants, and functional genomics data, the study has been mapped to produce a complete and comprehensive reference sequence for all 24 human chromosomes. Notably, both short and long read sequencing technologies have been utilized in this study to structure the entire human Y chromosome by frames and details, highlighting the potency of this \u201Cshort + long\u201D strategy in future sequencings. Long-read overcomes those complicated tandem repeat region, copy numbers and high-ploidy, short-read polishes the genomic details such as SNP and sequence variants.\\xa0\"}),/*#__PURE__*/e(\"p\",{children:\"The more we dig, the more we understand those scientific mysteries, in the meanwhile arising more \u201CWhy\u201Ds to be answered in the Omic-verse of Biology.\"}),/*#__PURE__*/e(\"h3\",{children:\"About MGI\"}),/*#__PURE__*/t(\"p\",{children:[\"MGI Tech Co., Ltd. (MGI) is at the forefront of global innovation, actively contributing to life science through intelligent innovation. With a presence in over 100 countries and a customer base of 2600+, MGI's cutting-edge technology has been instrumental in the development of 736+ user patents, facilitating the creation of over 150 petabytes of data. The company's extensive portfolio includes \",/*#__PURE__*/e(n,{href:{webPageId:\"eZTQUyXy4\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"sequencing instruments\"})}),\", \",/*#__PURE__*/e(n,{href:{webPageId:\"NWP4c4LYR\"},nodeId:\"iWpv9NMzk\",openInNewTab:!1,smoothScroll:!1,children:/*#__PURE__*/e(\"a\",{children:\"automation instruments\"})}),\", reagents, and related products that cater to various sectors such as life science research, agriculture, precision medicine, and healthcare.\"]}),/*#__PURE__*/e(\"p\",{children:\"MGI is dedicated to advancing life science tools for the healthcare of the future. As of December 2021, the company's global presence has expanded to more than 100 countries and regions, serving over 1,300 international clients. With a workforce of over 2,900 professionals worldwide, including 5 centers for research and development and production facilities in Europe, MGI is committed to fostering innovation. 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