Biology subjects

Tabula Sapiens Consortium,

Publications and source records attributed to Tabula Sapiens Consortium,.

5 recordsLinked to original sources

bioRxiv2026

Single-cell splice isoform usage reveals distinct axes of cellular identity and senescence

Alternative splicing greatly expands the diversity of gene products encoded by the human genome. Single-cell transcriptomic atlases have characterized human cell types through gene-level expression, but short-read sequencing has limited the ability to resolve full-length isoforms and their functional consequences. Here, we present a cross-tissue single-cell long-read isoform atlas spanning 26 human tissues. We identify hundreds of thousands of novel isoforms along with their cell-type-specific usage, and discover that over one-third of expressed isoforms are absent from existing reference databases. We further demonstrate that isoform usage is a structured, measurable axis of cellular identity that is distinct from gene expression. Applying this framework to cellular senescence, we resolve p16INK4a and p14ARF transcripts from the CDKN2A locus in individual cells and uncover cell-type-dependent isoform remodeling associated with the p16INK4a senescence program. This isoform-resolved single-cell atlas offers a versatile framework to dissect the cellular logic of isoform regulation in senescence and beyond.

genomics↗

bioRxiv2026

Tabula Sapiens reveals the non-coding RNA landscape across 22 human organs and tissues

The biological significance of non-coding RNAs has been increasingly appreciated as their roles in various cellular processes are uncovered. However, single-cell transcriptomic profiling of human samples has focused primarily on protein-coding genes by targeting polyadenylated RNA transcripts, leaving the expression patterns of non-coding RNA underexplored. Here, we expand Tabula Sapiens to the non-coding transcriptome with single-cell and single-nucleus total RNA sequencing across 22 human organs and tissues. By simultaneously profiling both polyadenylated and non-polyadenylated transcripts, the resulting dataset enables joint analysis of the protein-coding and non-coding transcriptomes at single-cell and subcellular resolution. Using these data, we assessed the cell type specificity of non-coding genes and found that a greater proportion of non-coding genes are differentially expressed by single cell types compared to protein-coding genes. We then compared single-cell and single-nucleus data from the same samples to infer subcellular localization patterns, revealing cell type-dependent nuclear and cytoplasmic enrichment of specific non-coding RNAs. Next, we showed that tRNA repertoires are cell type-specific and that this specificity is not simply explained by differences in codon usage across cell types. Finally, we characterized dynamic expression patterns of non-coding RNAs across the cell cycle and senescence-associated cell states, identifying non-coding genes with putative roles in cell division and growth arrest. Our work establishes a resource for investigating the landscape of non-coding RNAs across a diverse set of human tissues and cell types.

cell biology↗

bioRxiv2024

Benchmarking Cell Type Annotation by Large Language Models with AnnDictionary

We developed an open-source package called AnnDictionary (https://github.com/ggit12/anndictionary/) to facilitate the parallel, independent analysis of multiple anndata. AnnDictionary is built on top of LangChain and AnnData and supports all common large language model (LLM) providers. AnnDictionary only requires 1 line of code to configure or switch the LLM backend and it contains numerous multithreading optimizations to support the analysis of many anndata and large anndata. We used AnnDictionary to perform the first benchmarking study of all major LLMs at de novo cell-type annotation. LLMs varied greatly in absolute agreement with manual annotation based on model size. Inter-LLM agreement also varied with model size. We find that LLM annotation of most major cell types to be more than 80-90% accurate, and will maintain a leaderboard of LLM cell type annotation at https://singlecellgpt.com/celltype-annotation-leaderboard. Furthermore, we benchmarked these LLMs at functional annotation of gene sets, and found that Claude 3.5 Sonnet recovers close matches of functional gene set annotations in over 80% of test sets.

bioinformatics↗

bioRxiv2022

Single Cell Transcriptomics Reveals the Hidden Microbiomes of Human Tissues

The human microbiome has been studied extensively across those sites in the body that are readily accessible to sampling. Internal organs and tissues, however, have remained largely unexplored and, in the absence of infectious disease, are widely assumed to be free of microorganisms. Using single-cell transcriptomic data from the Tabula Sapiens spanning 11 human organ donors, 19 tissue types, 400,000+ annotated cells, 100+ cell types, and [~]70 billion sequences, we created an atlas of the human tissue microbiome with cell type resolution across three domains of life which we refer to as the Tabula Sapiens Microbiome. Together with data from 8 additional donors derived from a different set of experimental techniques for validation, we identified sequences from numerous bacterial, viral and fungal species in human tissues. We mapped the likely microbial flow routes in the body from external-facing microbiomes and natural environments to internal tissues and tumors, demonstrating the existence of several unexpected routes. In the case of tumor microbiomes, we find a considerable number of bacterial genera found in tumors are detectable across tumor-free tissues, suggesting that tumor microbiomes are in part sourced from adjacent normal tissues, even those from distant sites. Finally, by increasing the resolution of sampling from tissues to cell types, we quantified the microbial load and diversity across different human cell types to reveal a network of host cell type and microbe associations. For example, we identified traces of both latent and active Epstein Barr Virus infections in various cell types such as splenic plasma cells. Broad exploration of the human tissue microbiomes may provide insights which ultimately are of clinical importance.

microbiology↗

bioRxiv2021

RNA splicing programs define tissue compartments and cell types at single cell resolution

More than 95% of human genes are alternatively spliced. Yet, the extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach that is agnostic to transcript annotation, to detect cell-type-specific regulated splicing in > 110K carefully annotated single cells from 12 human tissues. Using 10x data for discovery, 9.1% of genes with computable SpliZ scores are cell-type specifically spliced. These results are validated with RNA FISH, single cell PCR, and in high throughput with Smart-seq2. Regulated splicing is found in ubiquitously expressed genes such as actin light chain subunit MYL6 and ribosomal protein RPS24, which has an epithelial-specific microexon. 13% of the statistically most variable splice sites in cell-type specifically regulated genes are also most variable in mouse lemur or mouse. SpliZ analysis further reveals 170 genes with regulated splicing during sperm development using, 10 of which are conserved in mouse and mouse lemur. The statistical properties of the SpliZ allow model-based identification of subpopulations within otherwise indistinguishable cells based on gene expression, illustrated by subpopulations of classical monocytes with stereotyped splicing, including an un-annotated exon, in SAT1, a Diamine acetyltransferase. Together, this unsupervised and annotation-free analysis of differential splicing in ultra high throughput droplet-based sequencing of human cells across multiple organs establishes splicing is regulated cell-type-specifically independent of gene expression.

bioinformatics↗