Biology subjects

Pisco, A. O.

Publications and source records attributed to Pisco, A. O..

26 records · Page 2Linked to original sources

bioRxiv2020

Single-cell identity definition using random forests and recursive feature elimination (scRFE)

Single-cell RNA sequencing (scRNA-seq) enables the detailed examination of a cells underlying regulatory networks and the molecular factors contributing to its identity. We developed scRFE with the goal of generating interpretable gene lists that can accurately distinguish observations (single-cells) by their features (genes) given a metadata category of the dataset. scRFE is an algorithm that combines the classical random forest classifier with recursive feature elimination and cross validation to find the features necessary and sufficient to classify cells in a single-cell RNA-seq dataset by ranking feature importance. It is implemented as a Python package compatible with Scanpy, enabling its seamless integration into any single-cell data analysis workflow that aims at identifying minimal transcriptional programs relevant to describing metadata features of the dataset. We applied scRFE to the Tabula Muris Senis and reproduced established aging patterns and transcription factor reprogramming protocols, highlighting the biological value of scRFEs learned features. Author summaryscRFE is a Python package that combines a random forest classifier with recursive feature elimination and cross validation to find the features necessary and sufficient to classify cells in a single-cell RNA-seq dataset by ranking feature importance. scRFE was designed to enable straightforward integration as part of any single-cell data analysis workflow that aims at identifying minimal transcriptional programs relevant to describing metadata features of the dataset.

bioinformatics↗

bioRxiv2020

Single-cell transcriptional atlas of the Chinese horseshoe bat (Rhinolophus sinicus) provides insight into the cellular mechanisms which enable bats to be viral reservoirs

Bats are a major "viral reservoir" in nature and there is a great interest in not only the cell biology of their innate and adaptive immune systems, but also in the expression patterns of receptors used for cellular entry by viruses with potential cross-species transmission. To address this and other questions, we created a single-cell transcriptomic atlas of the Chinese horseshoe bat (Rhinolophus sinicus) which comprises 82,924 cells from 19 organs and tissues. This atlas provides a molecular characterization of numerous cell types from a variety of anatomical sites, and we used it to identify clusters of transcription features that define cell types across all of the surveyed organs. Analysis of viral entry receptor genes for known zoonotic viruses showed cell distribution patterns similar to that of humans, with higher expression levels in bat intestine epithelial cells. In terms of the immune system, CD8+ T cells are in high proportion with tissue-resident memory T cells, and long-lived effector memory nature killer (NK) T-like cells (KLRG1, GZMA and ITGA4 genes) are broadly distributed across the organs. Isolated lung primary bat pulmonary fibroblast (BPF) cells were used to evaluate innate immunity, and they showed a weak response to interferon {beta} and tumor necrosis factor- compared to their human counterparts, consistent with our transcriptional analysis. This compendium of transcriptome data provides a molecular foundation for understanding the cell identities, functions and cellular receptor characteristics for viral reservoirs and zoonotic transmission.

cell biology↗

bioRxiv2020

Discovering novel cell types across heterogeneous single-cell experiments

Although tremendous effort has been put into cell type annotation and classification, identification of previously uncharacterized cell types in heterogeneous single-cell RNA-seq data remains a challenge. Here we present MARS, a meta-learning approach for identifying and annotating known as well as novel cell types. MARS overcomes the heterogeneity of cell types by transferring latent cell representations across multiple datasets. MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the cell embedding space. The method annotates cells by probabilistically defining a cell type based on nearest landmarks in the embedding space. MARS has a unique ability to discover cell types that have never been seen before and annotate experiments that are yet unannotated. We apply MARS to a large aging cell atlas of 23 tissues covering the life span of a mouse. MARS accurately identifies cell types, even when it has never seen them before. Further, the method automatically generates interpretable names for novel cell types. Remarkably, MARS estimates meaningful cell-type-specific signatures of aging and visualizes them as trajectories reflecting temporal relationships of cells in a tissue.

bioinformatics↗

bioRxiv2019

Mouse Aging Cell Atlas Analysis Reveals Global and Cell Type Specific Aging Signatures

Aging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq dataset to systematically characterize gene expression changes during aging across diverse cell types in the mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types also change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. Scoring cells based on these shared aging genes allowed us to contrast the aging status of different tissues and cell types from a transcriptomic perspective. In addition, we identified genes that exhibit age-related expression changes specific to each functional category of tissue-cell types. All together, our analyses provide one of the most comprehensive and systematic characterizations of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.

bioinformatics↗

bioRxiv2019

Unifying single-cell annotations based on the Cell Ontology

Single cell technologies have rapidly generated an unprecedented amount of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, independently of whether the cells types are present or absent in the training data, suggesting that OnClass can be used not only as an annotation tool for single cell datasets but also as an algorithm to identify marker genes specific to each term of the Cell Ontology, offering the possibility of refining the Cell Ontology using a data-centric approach.

bioinformatics↗

bioRxiv2019

A Single Cell Transcriptomic Atlas Characterizes Aging Tissues in the Mouse

Aging is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death1. Despite rapid advances over recent years, many of the molecular and cellular processes which underlie progressive loss of healthy physiology are poorly understood2. To gain a better insight into these processes we have created a single cell transcriptomic atlas across the life span of Mus musculus which includes data from 23 tissues and organs. We discovered cell-specific changes occurring across multiple cell types and organs, as well as age related changes in the cellular composition of different organs. Using single-cell transcriptomic data we were able to assess cell type specific manifestations of different hallmarks of aging, such as senescence3, genomic instability4 and changes in the organisms immune system2. This Tabula Muris Senis provides a wealth of new molecular information about how the most significant hallmarks of aging are reflected in a broad range of tissues and cell types.

cell biology↗

bioRxiv2019

Persistent Features of Intermittent Transcription

Here we report statistical studies of single-cell mRNA counts from cells derived from different tissues of adult mice. By examining correlations between mRNA gene counts we find strong evidence that when genes are only observed in a small fraction of cells, this is as a consequence of intermittent transcription rather than of expression only in specialized cell types. Count statistics are used to estimate a peak transcription level for each gene, and a probability for the gene to be active in any given cell. We find that the peak transcription levels are approximately constant across different tissue types, but the gene expression probabilities may be markedly different. Both these quantities have very wide ranges of values, with a probability density function well approximated by a power law.\n\nAuthor summaryUsing evidence from single-cell mRNA counts, we argue that the expression of many genes in individual mouse cells is highly intermittent. Comparing cells from different tissues, we find that the peak activity of a given gene is approximately the same in all tissue types, whereas the probability of a gene being active can differ markedly.

genomics↗

bioRxiv2017

A protein phosphatase network controls temporal and spatial dynamics of differentiation commitment in human epidermis

Epidermal homeostasis depends on a balance between stem cell renewal and terminal differentiation. The transition between the two cell states, termed commitment, is poorly understood. Here we characterise commitment by integrating transcriptomic and proteomic data from disaggregated primary human keratinocytes held in suspension for up to 12h to induce differentiation. We find that cell detachment induces a network of protein phosphatases. The pro-commitment phosphatases - including DUSP6, PPTC7, PTPN1, PTPN13 and PPP3CA - promote differentiation by negatively regulating ERK MAPK and positively regulating AP1 transcription factors. Their activity is antagonised by concomitant upregulation of DUSP10. Boolean network modelling of phosphatase interactions identifies commitment as an inherently unstable biological switch between the stem and differentiated cell states. Furthermore, phosphatase expression is spatially regulated both in vivo and in vitro. We conclude that an auto-regulatory phosphatase network maintains epidermal homeostasis by controlling the onset and duration of commitment.

developmental biology↗