Applications of Rna Sequencing

Probably, one of the largest collaborative efforts in biotech, since the Human Genome Project: the Human Cell Atlas has arrived! 🧬 I think the Human Cell Atlas (HCA) is a pretty monumental leap in systems biology, an international effort involving 3,600 researchers from 102 countries, has released its first draft atlas of human cells. This isn’t just another dataset—this is the blueprint of human biology, built cell by cell, tissue by tissue, organ by organ. The HCA integrated data from 62 million cells, sourced from 9,100 donors, spanning every stage of human development—embryonic to adult. Researchers organized their work into 18 Biological Networks, focusing on key organs like the lung, nervous system, and eye. Some of the tools like single-cell RNA sequencing, spatial transcriptomics, and multi-omics were combined to profile and map cells with unprecedented precision. Notably, Google provided essential cloud infrastructure and AI tools like scTab (for annotation) and SCimilarity (for cell similarity searches), helping researchers handle vast and complex datasets efficiently. It is also important that local scientists and the HCA Ethics Working Group put efforts to make sure data represented populations globally, prioritizing equity and open access. Now, how can we use it, practically speaking? Here I picked some of the key aspects that might be very useful for the biotech community: ✅ Precise Target Discovery: Pinpoint disease-specific cell types and biomarkers to create highly targeted therapies. ✅ Better Disease Models: Build realistic organoids and in vitro models informed by detailed cell maps for accurate drug testing. ✅ Personalized Medicine: Utilize data from diverse populations to design therapies tailored to genetic and environmental variations. ✅ Safer Drugs: Analyze tissue-specific metabolism to predict and avoid adverse drug effects. ✅ AI-Driven Insights: Tap into machine-learning tools like PopV and SCimilarity to accelerate discovery and refine findings. I believe, the Atlas could be a playing ground for other AI tools and new workflows! ✅ Early Diagnosis: Identify subtle gene expression changes for early detection of diseases like cancer or neurodegenerative disorders. If you're in biotech, drug discovery, or systems biology, this resource is now open and available—check it out! Link in the comments 👇 Image source:  Springer Nature

Every test came back normal. For 10 years, a family kept searching. DNA sequencing found nothing. Standard panels found nothing. The family kept searching. Then researchers at CHOP (Children's Hospital of Philadelphia) applied long-read RNA sequencing and finally saw what was hiding in the repetitive regions that routine tests skip. What does this mean?? Think of it like reading a page with stuttering text (the the the same same same words). Normal genetic tests skip right over those parts. This new test can actually read through all the repetition and find problems hiding there. Imagine, ten years to find one answer. Unfortunately, this isn't an isolated case. Less than 30% of rare disease patients can identify the specific genetic cause - even after DNA testing. The problem isn't that we're not testing enough. The problem is that DNA sequencing alone can't show us how variants affect RNA processing. Here's why: • DNA = the blueprint • RNA = what your cells actually build from that blueprint • DNA sequencing shows the written instructions • RNA sequencing shows what happens when cells try to follow them A variant can look perfectly normal in the DNA blueprint - but when cells try to build from it, the process breaks down. And standard DNA tests never see it. This creates a catch-22 for patient access. Payers want genetic confirmation before approving high-cost therapies. But if standard sequencing can't deliver that confirmation, patients stay in limbo. No diagnosis means no treatment eligibility. No eligibility means no access. The CHOP team built STRIPE to close this gap. It reads RNA from easy-to-collect samples like blood and skin. Lower cost than full transcriptome sequencing. Deep enough coverage to catch low-expression disease genes. They've now deployed it in 500+ patients across multiple rare disease programs. Four patients had genetic variants that were previously labeled 'uncertain' - now confirmed as the cause of their disease. Four families who had questions got answers. This is how we close diagnostic gaps that block treatment access. If you're building evidence strategies for genetic therapies, are you accounting for the patients who need RNA-level confirmation to qualify? Follow Dr. Suzanne Morgan for AI Innovation for Rare Disease

Can nanopore sequencing revolutionize early disease detection? Current liquid biopsy techniques utilizing cell-free DNA often struggle to identify early-stage diseases due to their limited sensitivity. A recent groundbreaking study by Vikas Peddu, Karen Miga, Rebecca Fitzgerald, Daniel Kim, and their team from the University of California, Santa Cruz, and the University of Cambridge, published on bioRxiv, showcases the potential of long-read nanopore sequencing to be a game-changer. The researchers analyzed full-length cell-free RNA (cfRNA) from plasma samples to differentiate between healthy individuals, those with precancerous Barrett’s esophagus, and patients with esophageal adenocarcinoma. They discovered 270,679 novel intergenic cfRNAs and developed a tailored transcriptome reference for precise classification of both precancerous and cancerous conditions. Additionally, they pinpointed potential therapeutic targets within metabolic, signaling, and immune checkpoint pathways. These results highlight the efficacy of nanopore-based long-read RNA liquid biopsy platforms in early disease detection and targeted treatment, surpassing the capabilities of conventional methods. The methods developed in this study should be adaptable to detect other types of cancers using cfRNA in plasma. Exciting developments lie ahead in the realm of precision oncology! To delve deeper into the study, access the paper here: https://lnkd.in/erGvy_Wk #LiquidBiopsy #cfRNA #NanoporeSequencing #EarlyDetection #CancerDiagnostics #PrecisionMedicine #OncologyResearch

Large-scale biomedical datasets that combine histology with paired RNA sequencing (RNA-seq) and Whole Genome Sequencing (WGS) data creates the opportunity to understand ways that somatic mutations and variation in gene expression influence tissue-level properties in health and disease. A new study led by Francesco Cisternino details the development of a Vision Transformer trained on 1.7 million histology images across 23 healthy tissue types that can be used for automatic tissue segmentation and the prediction of spatially localized RNA expression levels from H&E histology images. The authors show that by learning self-supervised representations from a large set of histology images across healthy tissues, the model can automatically identify tissue substructures and pathologies without labels. Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types. https://lnkd.in/e6i_sb8S Methods overview: The authors used 13,898 whole slide images from 23 tissue types in 838 donors from the GTEx project. They preprocessed the images by segmenting the tissue from background using a U-net, and tiled the tissue into 63 x 63 μm2 regions. They trained a small Vision Transformer (ViT-S) using the self-supervised DINO framework on 1.7 million GTEx histology tiles to extract morphological features.  Using the learned features, they classified tiles through a K-Nearest Neighbors model to derive phenotypes in terms of extent of detected regions. The RNAPath model takes tile embeddings as input and predicts both local (tile-level) and global (sample-level) gene expression as output, along with a heatmap to visualize predicted spatial gene activity. Results overview: The self-supervised DINO embeddings show better qualitative clustering and 43% improvement in silhouette score compared to other representation learning methods. Using a kNN approach, they are able to automatically segment whole slide images into constituent tissue substructures and pathology proportions with high accuracy. They find substantial variability in tissue substructure proportions across donors within the same tissue type. Using the substructure and pathology proportions, they identify profound tissue variability across donors that drives substantial differential gene expression, as well as characterize germline genetic variants associated with specific histopathological features. The RNAPath models are able to predict individual RNA expression levels from histology with superior performance to competing methods. They validate RNAPath spatial predictions using positive control immunohistochemistry and characterize the localized expression signatures of 29 individual substructures and pathologies. Both the histology tile representations and RNAPath generalize well to an external validation cohort, TCGA-BRCA, demonstrating ability to segment carcinoma from benign tissue.

Bioinformatics gold is often found in the junk pile. Here’s how to mine it. 1/ You delete them without thinking. Unmapped reads. But buried in the trash... is treasure. Let me explain. 2/ Unmapped RNA-seq reads aren’t useless. They often hold microbial RNA. Think: hidden infections, gut bugs, or viral contaminants. 3/ You can recover microbial reads from human RNAseq That’s data most people throw away. Don’t be most people. 4/ Or take circular RNAs (circRNAs). Standard aligners can’t map back-spliced reads. But those “unmapped” reads? They are the circRNAs. 5/ circRNAs regulate gene expression and are potential biomarkers for cancer, brain disorders, and more. 6/ reads can not be mapped to the highly variable region of TCR and BCR genes, but you can use tools such as https://lnkd.in/e-5jT5MS to reconstruct the TCR and BCR sequences using the unmapped reads. Those are valuable data for studying the immune response. I used it! 7/ RNA-seq is more than gene counts. You can call SNPs and somatic mutations. Even BRAF V600E shows up if you know where to look. 8/ You don’t always need DNA-seq to call variants. RNA-seq gives you heterozygous SNPs (like rsIDs) in expressed regions—cheaply. I identified sample swaps for multi-omics studies with both RNAseq and WES data by calling SNPs from RNAseq data and mapping them to the WES SNPs. 9/ You can even phase reads and detect allele-specific expression (ASE). Want to study imprinting? IGF2, H19? Start here. 10/ ASE tells you which allele is active—maternal or paternal. It reveals silencing, imprinting, or regulatory variation. That’s power. 11/ Whole-exome sequencing (WES)? not only for detecting mutations. Use coverage data to detect copy number variants (CNVs). 12/ ATAC-seq isn’t just for open chromatin. You can extract CNV patterns from it, especially in tumors. or detect ecDNA too! 13/ Even repeats have meaning. Unmapped RNA-seq reads often come from LINE/SINE retrotransposons. Like Alu or L1. 14/ These elements are noisy—but informative. Dysregulated transposons are linked to neurodegeneration, cancer, aging. 15/ You can even use RNAseq data to determine variable 3UTR length and that has implications in cancer too! 14/ Key takeaways: Unmapped ≠ useless RNA-seq is a multi-tool: SNPs, circRNA, microbes, ASE, 3UTR length WES & ATAC-seq reveal CNVs Repeats matter Bioinformatics rewards the curious. Even in the trash. I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter chatomics to learn bioinformatics https://lnkd.in/erw83Svn

🟥 Spatially Resolved Single-Cell Atlas of Organ Development and Regeneration Understanding the mechanisms of organ development and regeneration requires a deep understanding of the spatial and cellular architecture of tissues over time. Recent advances in spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) have enabled the construction of high-resolution atlases that map gene expression within intact tissues at the cellular level. These atlases provide an unprecedented view of the molecular architecture and dynamic changes of organs during embryogenesis, postnatal development, and injury-induced regeneration. Spatially resolved single-cell atlases combine positional information with transcriptomic lineage information, allowing researchers to identify specific cell types, track their developmental trajectories, and understand their interactions in tissue microenvironments. For example, during the development of the heart, liver, and kidney, these atlases reveal transient progenitor populations, regionalized signaling gradients, and lineage differentiation that regulate tissue morphogenesis. During regeneration, such as in liver or skin injury models, spatial single-cell analysis can reveal cellular plasticity, reactivation of developmental programs, and microenvironmental remodeling processes that guide tissue repair. Importantly, spatial single-cell atlases can also facilitate the identification of signaling hubs and intercellular communication networks by capturing ligand-receptor interactions in situ. This spatial context is critical for understanding how stem cells are maintained, how differentiation is spatially regulated, and how inflammatory or fibrotic responses are initiated during regeneration. In addition, by comparing normal development with regeneration and pathological states, researchers can uncover deviations in cell fate decisions or signaling that lead to disease. Overall, spatially resolved single-cell atlases are foundational resources for developmental biology, regenerative medicine, and disease modeling. They provide a blueprint for designing targeted interventions that promote regeneration or prevent fibrosis and tissue degeneration. As the technical resolution and scalability improve, these atlases will play an increasingly important role in guiding stem cell engineering, organoid design, and precision medicine strategies. Reference [1] Jie Liao et al., Trends in Biotechnology 2021 (DOI: 10.1016/j.tibtech.2020.05.006) #SpatialTranscriptomics #SingleCellAtlas #OrganDevelopment #RegenerativeMedicine #StemCells #TissueEngineering #PrecisionMedicine #Bioinformatics #DevelopmentalBiology #BiomedicalResearch #CSTEAMBiotech

A decade ago, molecular measurement was shorthand for Illumina or 10X. Today, its a composable stack of pluggable technologies. Pick a prep that plays well with source material, an assay that captures the relevant biology (chromatin marks, isoforms, long range structure), a sequencer that matches budget/throughput and an analysis portal to close the loop. As the ecosystem grows, new players often don't outright replace incumbents, but complement them, sitting upstream or downstream: eg. FFPE paraffin removal (Covaris), modified-base sequencing (biomodal), instrument-free single-cell (Parse Biosciences), long range chromatin structure (Dovetail Genomics), secondary/tertiary analysis tools (LatchBio). Thinking about how a particular tool, kit or service slots into the end-to-end lifecycle from raw material to insight is useful way to organize this ecosystem. 1/ Pre-analytics and library chemistry: - Covaris: extraction/shearing incl. FFPE - Claret Bioscience: SRSLY for damaged/low-input DNA; REALLY for directional RNA-seq with rRNA or globin depletion 2a/ Molecular capture: - Parse Biosciences: instrument-free split-pool single-cell (Evercode) - ActiveMotif: CUT+RUN / CUT+Tag; single-cell epigenomics - Takara Bio USA, Inc.: SMART-Seq for full-length single-cell / nuclei transcriptomes - biomodal: duet multiomics (evoC/+modC) for sequence + 5mC/5hmC in one workflow - 10x Genomics: literally so much stuff 2b/ Structural capture: Dovetail Genomics: Omni-C for scaffolding, phasing, SVs/3D Nabsys: electronic genome mapping to confirm/resolve SVs (to ~300 bp) 3/ Sequencers ("readout" machines): Element Biosciences: AVITI; benchtop, high-accuracy with flexible cost/throughput Complete Genomics: DNBSEQ; G99 for fastest runs; G800 for 600 bp SE option 4/ Downstream misc. SPT Labtech firefly / firefly+ for compact, walk-away NGS prep Teiko high-dimensional cytometry for clinical trials Signios Biosciences bioinformatics services 5/ Data and analysis delivery LatchBio secondary + tertiary analysis; closing the loop and allowing biologist to finally answer end scientific question Specialization and composability allows individual engineering teams to go deep, pushing cost, accuracy and ergonomics of the end-to-end measurement workflows large leaps forward. It is not obvious the structure of the ecosystem should develop in this way and something folks should pay attention to. Every company mentioned here, along with a few hundred scientists, will be gathering at Latch HQ (Mission Bay, SF) this Thursday to talk science and technology over drinks and food. Link below. Come join.

Put on your dive gear: we're going deep on single-cell and spatial transcriptomics methods! Single-Cell Methods: FACS - Fluorescence-activated Cell Sorting isolates cells using fluorescence and laser deflection by staining cells with a dye or by tagging them with antibodies. Capable of sorting >100 cells per experiment. Examples: Smart-Seq, MATQ-Seq, and CEL-Seq Microdroplets - Single-cells are isolated by flowing cells and reagents through a device to create single-cell containing oil microdroplets. In many cases a bead covered in poly-T sequences is used to capture the 3’-end of RNA transcripts. Cannot recover full-length transcripts. 10,000+ cells at a time, >50% recovery. Examples: 10x Chromium, Complete Genomics DNBelab, and Drop-Seq Microwells - Instead of isolating cells in droplets, microfluidics or limiting dilution are used to sequester cells into microscopic wells on a plate or a slide. This technique also allows for the sequencing of full-length transcripts. Examples: BD Rhapsody (10,000+ cells), Fluidigm C1 (800+ cells) Combinatorial Barcoding - The latest advancement in single-cell is the use of combinatorial barcoding to label transcripts within fixed cells without having to use any fancy or expensive instruments up-front. In-cell reverse transcription with a barcoded primer is performed followed by two rounds of splitting the cells into new 96 well plates and ligating new barcodes. A 4th barcoding split is done using a PCR reaction. 10,000+ cells at a time, <50% recovery. Examples: SPLiT-Seq, Parse Biosciences, Scale Biosciences. Spatial Methods: Microdissection - Prepared histology slides can be microdissected in two different ways. Traditional laser capture microdissection can be performed and regions sequenced. Alternatively, slides can be labeled with oligo tagged RNAs or antibodies and then those sequence tags released by the exposure of regions of the slide to UV light. Sequencing of the tags tells you which RNAs/Proteins were present in each dissected region. Example: Leica, etc (Laser Capture), Nanostring GeoMx (UV) Microarray - Histology slides can also be overlaid with RNA capture arrays like the Illumina beadarray to divine spatial information. This was introduced as Slide-seq but has been commercialized by 10x as Visium. Multiplex FISH - This is basically Fluorescence In-Situ Hybridization on steroids. Histology slides are prepared and exposed to multiple fluorescently tagged RNA probes. Multiple rounds of fluorescent tagging followed by imaging allows for the detection of gene specific signals at the cellular level. Examples: Vizgen/UltiVue MERFISH, Nanostring CosMx In-Situ Sequencing - Uses sequencing technology to extend random or transcript specific primers in a rolling circle amplification reaction. Sequencing proceeds using 1-2 base labeled probes with imaging after every cycle of probe addition and provides cellular transcript localization. Examples: 10x Xenium, Element Teton

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular diversity, allowing researchers to explore cellular heterogeneity with unprecedented detail. To interpret the vast data generated by scRNA-seq, researchers often rely on three key analytical approaches: Gene Ontology (GO) analysis, pathway analysis, and Gene Set Enrichment Analysis (GSEA). These methods collectively fall under the umbrella of functional enrichment analysis, which encompasses various computational techniques aimed at identifying and interpreting the biological functions, processes, and pathways that are overrepresented or significant in a given set of genes or proteins. GO analysis offers detailed functional characterization by identifying specific biological processes and functions affected by treatments. It highlights how differentially expressed genes (DEGs) are related to various biological roles. Pathway analysis, on the other hand, provides systemic insights by examining how DEGs interact within biological pathways, pinpointing key signaling and metabolic pathways influenced by the treatment. GSEA takes a broader approach, assessing gene set enrichment across the entire dataset to capture coordinated, global changes and validate findings from the other analyses. Employing all three methods can offer a more complete picture of the biological impact of treatments. While GO and pathway analyses provide specific insights into particular processes and pathways, GSEA reveals overarching trends and functional shifts. This combination of broad and specific analyses aids in generating and validating hypotheses about how treatments affect biological functions and pathways, strengthening the overall interpretation of the data. In my latest tutorial, linked below, we’ll explore the purpose, benefits, and applications of each approach, using skeletal muscle research as an example. However, it should be noted that all of the analyses discussed in this article have broader relevance in other fields, such as cancer research. Additionally, I’ll show you how to perform each analysis, using real-world scRNA-seq data. 👉 https://lnkd.in/eJWC_rHB #bioinformatics #compbio #biotech #rnaseq #datascience 

Why do you need to know about cutting-edge tests and AI for finding your best treatment options? - RNA sequencing (which reads expression of ~20,000 genes) gives a broad, real‑time picture of your tumor biology that standard DNA panels or single biomarkers miss; it can identify likely drug sensitivity or resistance even when no actionable DNA mutations are present, enabling treatment options for you if you otherwise lack clear targets. - AI matching rapidly analyzes your detailed tumor biology to rank your most likely effective treatments, giving you and your medical team focused, personalized options faster than traditional methods - Functional tests (patient‑derived organoids from blood or tissue) can validate AI predictions in the lab to see which drugs actually kill your tumor cells and lower the risk of trying toxic, ineffective treatments For more from our conversation with Precision AI Solutions Co-founder and CEO Edwin Alphonso, CSO Sophia Ren, and Cellentia Research Partner Dr. SJ Shih on ways to use RNA-seq, AI, and functional testing for marking precise cancer care decisions, please see https://lnkd.in/eQUXCGQ2