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Biology subjects

Pisco, A. O.

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

At least 19 recordsLinked to original sources
bioRxiv2026

Upregulation of Genetic Markers of Poor Prognosis following Chemotherapy in Acute Myeloid Leukemia Cells

Chemoresistance, a leading cause of treatment failure in cancer, is commonly explained by Darwinian selection of treatment-resistant cell clones. However, recurring resistant tumors invariably display complex phenotypes that contribute to increased malignancy, unlikely to have been selected for by chemotherapy. The growing awareness of (non - genetic) phenotypic plasticity led to the hypothesis that chemotherapy, while reducing tumor burden, also inflicts cell stress that induces stem-like states in cells that survive treatment. Here we examined the transcriptomes of HL-60 leukemic cancer cells that survived exposure to three commonly used drugs at submaximal doses for two to four days, and compared differentially expressed genes to those associated with prognosis in public transcriptome databases of acute myeloid leukemia cohorts. While among the genes differentially upregulated in surviving cells, some reflect the therapeutic effect of chemotherapy as they were associated with favorable outcomes in cohort data, many genes upregulated were associated with poor survival, notably genes involved in stemness, epithelial-mesenchymal transition (EMT), inflammation, drug resistance, and apoptosis evasion. These findings support the idea that treatment effectiveness is the net result of an intrinsic tradeoff: Cytocidal treatment, intended to quantitatively reduce cancer cells, also qualitatively increases the malignancy of non-killed cells, which could contribute to residual disease and relapse. This result has implications for drug screening of new therapeutics, as well as in vitro profiling of patient-derived tumor cell susceptibility to existing drugs, which only assess suppression of cancer cell growth and survival. Statement of SignificanceCells surviving chemotherapy upregulate many genes that may affect prognosis. Therefore, drug screening must embrace a more holistic assessment of the biological quality of cell response beyond the rate of cell killing.

cancer biology↗
bioRxiv2024

How Mycobacterium tuberculosis builds a home: Single-cell analysis reveals M. tuberculosis ESX-1-mediated accumulation of anti-inflammatory macrophages in infected mouse lungs.

Mycobacterium tuberculosis (MTB) infects and replicates in lung mononuclear phagocytes (MNPs) with astounding ability to evade elimination. ESX-1, a type VII secretion system, acts as a virulence determinant that contributes to MTBs ability to survive within MNPs, but its effect on MNP recruitment and/or differentiation remains unknown. Here, using single-cell RNA sequencing, we studied the role of ESX-1 in MNP heterogeneity and response in mice and murine bone marrow-derived macrophages (BMDM). We found that ESX-1 is required for MTB to recruit diverse MNP subsets with high MTB burden. Further, MTB induces an anti-inflammatory signature in MNPs and BMDM in an ESX-1 dependent manner. Similarly, spatial transcriptomics revealed an upregulation of anti-inflammatory signals in MTB lesions, where monocyte-derived macrophages concentrate near MTB-infected cells. Together, our findings suggest that MTB ESX-1 mediates the recruitment and differentiation of anti-inflammatory MNPs, which MTB can infect and manipulate for survival.

microbiology↗
bioRxiv2024

Multimodal classification of neurons in the lateral septum

The lateral septum (LS) is a ventral forebrain nucleus that modulates complex social and affective behaviors. These behaviors emerge from heterogeneous neuronal populations whose molecular identity and developmental origins remain poorly defined. We profiled the transcriptional identity of mature LS neurons derived from two progenitor lineages distinguished by their embryonic origin and Nkx2.1 expression history, identifying 22 molecularly distinct subtypes. Nkx2.1-lineage neurons are enriched for select cell adhesion and communication molecules; however, subtypes from distinct developmental origins can converge onto similar molecular profiles when residing within the same LS subregion. The graded expression of genes related to synaptic signaling is a primary axis defining the taxonomy of LS neurons. Using transcriptional markers, we labeled non-overlapping neuronal populations and characterized their connectivity, morphology, and electrophysiology. Together, these findings define the extent of LS neuronal diversity and provide a framework for understanding how complex behaviors are regulated by the LS.

neuroscience↗
bioRxiv2023

Single-nucleus RNA sequencing provides insights into the GL261-GSC syngeneic mouse model of glioblastoma

Glioblastoma (GBM) is an aggressive tumor with very bad prognosis. The urgent need to find new effective therapies is challenged by the unique characteristics of GBM, including high intra and intertumoral heterogeneity. Using single-nucleus transcriptomics (snRNA-seq), we characterized the panorama of a preclinical immunocompetent murine model based in the implantation of mouse glioblastoma stem cells (GL261-GSCs) into the brain parenchyma. Additionally, we performed Visium spatial transcriptomics in the in vivo model to confirm the location of annotated cells. To understand the technical bias of this approach, we performed two scRNA-seq methods in GBM cells. We thoroughly characterized the tumor microenvironment (TME) at early and late stages of tumor development and upon treatment with temozolomide (TMZ), the standard of care for patients with GBM, and with Tat-Cx43266-283, a promising experimental treatment. We identified prominent GBM targets that can be addressed using this preclinical model, such as Grik2, Nlgn3, Gap43 or Kcnn4, which are involved in electrical and synaptic integration of GBM cells into neural circuits, as well as the expression of Nt5e, Cd274 or Irf8, which indicates the development of immune evasive properties in these GBM cells. In agreement, snRNA-seq unveiled high expression of several immunosuppressive-associated molecules in immune cells, such as Csf1r, Arg1, Mrc1 and Tgfb1, suggesting the development of an immunosuppressive microenvironment. We also show the landscape of cytokines, cytokine receptors, checkpoint ligands and receptors in tumor and TME cells, which are crucial data for a rational design of immunotherapy studies. Thus, Mrc1, PD-L1, TIM-3 or B7-H3 are among the immunotherapy targets that can be addressed in this model. Finally, the comparison of the preclinical GL261-GSC GBM model with human GBM subtypes unveiled important similarities with the recently identified TMEmed human GBM, indicating that preclinical data obtained in GL261-GSC GBM model might be applied to TMEmed human GBM, improving patient stratification in clinical trials. In conclusion, this work provides crucial information for future preclinical studies in GBM improving their clinical application.

neuroscience↗
bioRxiv2023

Developmental origin and local signals cooperate to determine septal astrocyte identity

Astrocyte specification during development is influenced by both intrinsic and extrinsic factors, but the precise contribution of each remains poorly understood. Here we show that septal astrocytes from Nkx2.1 and Zic4 expressing progenitor zones are allocated into non-overlapping domains of the medial (MS) and lateral septal nuclei (LS) respectively. Astrocytes in these areas exhibit distinctive molecular and morphological features tailored to the unique cellular and synaptic circuit environment of each nucleus. Using single-nucleus (sn) RNA sequencing, we trace the developmental trajectories of cells in the septum and find that neurons and astrocytes undergo region and developmental stage-specific local cell-cell interactions. We show that expression of the classic morphogens Sonic hedgehog (Shh) and Fibroblast growth factors (Fgfs) by MS and LS neurons respectively, functions to promote the molecular specification of local astrocytes in each region. Finally, using heterotopic cell transplantation, we show that both morphological and molecular specifications of septal astrocytes are highly dependent on the local microenvironment, regardless of developmental origins. Our data highlights the complex interplay between intrinsic and extrinsic factors shaping astrocyte identities and illustrates the importance of the local environment in determining astrocyte functional specialization.

neuroscience↗
bioRxiv2023

Consensus prediction of cell type labels with popV

Cell-type classification is a crucial step in single-cell analysis. To facilitate this, several methods have been proposed for the task of transferring a cell-type label from an annotated reference atlas to unannotated query data sets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness. To address this, we propose popular Vote (popV, https://github.com/YosefLab/popV), an ensemble of prediction models with an ontology-based voting scheme. PopV achieves accurate cell-type labeling and provides effective uncertainty scores. In multiple case studies, popV confidently annotates the majority of cells while highlighting cell populations that are challenging to annotate. This additional step helps to reduce the load of manual inspection, which is often a necessary component of the annotation process, and enables one to focus on the most problematic parts of the annotation, streamlining the overall annotation process.

bioinformatics↗
bioRxiv2023

Tutorial: guidelines for manual cell type annotation of single-cell multi-omics datasets using interactive software

Assigning cell identity to clusters of single cells is an essential step towards extracting biological insights from many genomics datasets. Although annotation workflows for datasets built with a single modality are well established, limitations exist in annotating cell types in datasets with multiple modalities due to the need for a framework to exploit them jointly. While, in principle, different modalities could convey complementary information about cell identity, it is unclear to what extent they can be combined to improve the accuracy and resolution of cell type annotations. Here, we present a conceptual framework to examine and jointly interrogate distinct modalities to identify cell types. We integrated our framework into a series of vignettes, using immune cells as a well-studied example, and demonstrate cell type annotation workflows ranging from using single-cell RNA-seq datasets alone, to using multiple modalities such as single-cell Multiome (RNA and chromatin accessibility), CITE-seq (RNA and surface proteins). In some cases, one or other single modality is superior to the other for identification of specific cell types, in others combining the two modalities improves resolution and the ability to identify finer subpopulations. Finally, we use interactive software from CZ CELLxGENE community tools to visualize and integrate histological and spatial transcriptomic data.

bioinformatics↗
bioRxiv2022

Aging Fly Cell Atlas Identifies Exhaustive Aging Features at Cellular Resolution

Aging is characterized by a decline in tissue function, but the underlying changes at cellular resolution across the organism remain unclear. Here, we present the Aging Fly Cell Atlas, a single-nucleus transcriptomic map of the whole aging Drosophila. We characterize 163 distinct cell types and perform an in-depth analysis of changes in tissue cell composition, gene expression, and cell identities. We further develop aging clock models to predict the fly age and show that ribosomal gene expression is a conserved predictive factor for age. Combining all aging features, we find unique cell type-specific aging patterns. This atlas provides a valuable resource for studying fundamental principles of aging in complex organisms.

developmental biology↗
bioRxiv2022

Mouse lemur transcriptomic atlas elucidates primate genes, physiology, disease, and evolution

Mouse lemurs (Microcebus spp.) are an emerging primate model organism. However, little is known about their genetics or cellular and molecular biology. In the accompanying paper, we used large-scale single cell RNA-sequencing of 27 organs and tissues to identify over 750 molecular cell types, characterize their full transcriptomic profiles, and study evolution of primate cell types. Here we use the atlas to characterize mouse lemur genes, mutations, physiology, and disease. We uncover thousands of previously unidentified lemur genes and hundreds of thousands of new splice junctions that globally define lemur gene structures and reveal over 85,000 primate splice junctions missing in mice. We systematically explore the lemur immune system, comparing the global expression profiles of key immune genes in health and disease, and molecular mapping of immune cell development, trafficking, and their local and global activation to infection. We characterize primate/lemur-specific physiology and disease including molecular features of the immune program, of lemur adipocytes that exhibit dramatic seasonal rhythms, and of metastatic endometrial cancer that resembles the human malignancy. We identify and describe the expression patterns of over 400 primate genes missing in mice, many with similar expression patterns in human and lemur and some implicated in human disease. Finally, we provide an experimental framework for reverse genetic analysis by identifying naturally-occurring nonsense (null) mutations in three primate genes missing in mice and analyzing their transcriptional phenotypes. This work establishes mouse lemur as a tractable primate model organism for genetic and molecular analysis, and it prioritizes primate genes, splice junctions, physiology, and disease for future study.

genetics↗
bioRxiv2022

Dissecting the contributions of tumor heterogeneity on metastasis at single-cell resolution

Metastasis is the leading cause of cancer-related deaths, but metastasis research is challenged by limited access to patient material and a lack of experimental models that appropriately recapitulate tumor heterogeneity. Here, we analyzed single-cell transcriptomes of matched primary tumor and metastasis from patient-derived xenograft models of breast cancer, demonstrating that primary tumor and metastatic cells show profound transcriptional differences across heterogeneous tumors. While primary tumor cells upregulated several metabolic genes, metastatic cells displayed a motility phenotype in micrometastatic lesions and increased stress response signaling during metastatic progression. Additionally, we identified gene signatures that are associated with the metastatic potential and correlated with patient outcomes. Poorly metastatic primary tumors showed increased immune-regulatory control that may prevent metastasis, whereas highly metastatic primary tumors upregulated markers of epithelial-mesenchymal transition (EMT). We found that intra-tumor heterogeneity is dominated by epithelial-mesenchymal plasticity (EMP) which presented as a dynamic continuum with intermediate cell states that were characterized by novel, specific markers. These intermediate EMP markers correlated with worse patient outcomes and could serve as potential new therapeutic targets to block metastatic development.

cancer biology↗
bioRxiv2022

Comprehensive characterization of the transcriptional response to COVID-19 in multiple organs reveals shared signatures across tissues

Infection by Coronavirus SARS-CoV2 is a severe and often deadly disease that has implications for the respiratory system and multiple organs across the human body. While the effects in the lung have been extensively studied, less is known about COVID-19s cellular impact across other organs. Here we contribute a single-nuclei RNA sequencing atlas comprising six human organs across 20 autopsies where we analyzed the transcriptional changes due to COVID-19 in multiple cell types. Computational cross-organ analysis for endothelial cells and macrophages identified systemic transcriptional changes in these cell types in COVID-19 samples. In addition, analysis of signaling pathways from multiple datasets showed several systemic dysregulations of signaling interaction in different cell types. Altogether, the COVID Tissue Atlas enables the investigation of both cell type-specific and cross-organ transcriptional responses to COVID-19, providing insights into the molecular networks affected by the disease and highlighting novel potential targets for therapies and drug development.

genomics↗
bioRxiv2022

Multimodal single cell data integration challenge: results and lessons learned

Biology has become a data-intensive science. Recent technological advances in single-cell genomics have enabled the measurement of multiple facets of cellular state, producing datasets with millions of single-cell observations. While these data hold great promise for understanding molecular mechanisms in health and disease, analysis challenges arising from sparsity, technical and biological variability, and high dimensionality of the data hinder the derivation of such mechanistic insights. To promote the innovation of algorithms for analysis of multimodal single-cell data, we organized a competition at NeurIPS 2021 applying the Common Task Framework to multimodal single-cell data integration. For this competition we generated the first multimodal benchmarking dataset for single-cell biology and defined three tasks in this domain: prediction of missing modalities, aligning modalities, and learning a joint representation across modalities. We further specified evaluation metrics and developed a cloud-based algorithm evaluation pipeline. Using this setup, 280 competitors submitted over 2600 proposed solutions within a 3 month period, showcasing substantial innovation especially in the modality alignment task. Here, we present the results, describe trends of well performing approaches, and discuss challenges associated with running the competition.

bioinformatics↗
bioRxiv2022

Comparative analysis of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

Spatial transcriptomics extends single cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) can achieve single-cell resolution, directly mapping single cell identities to spatial positions. MERFISH produces an intrinsically different data type than scRNA-seq and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas as well as from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that compared to scRNA-seq, MERFISH provides a quantitatively comparable method for measuring single-cell gene expression and can robustly identify cell types without the need for computational integration with scRNA-seq reference atlases.

bioinformatics↗
bioRxiv2021

Tabula Microcebus: A transcriptomic cell atlas of mouse lemur, an emerging primate model organism

Mouse lemurs are the smallest, fastest reproducing, and among the most abundant primates, and an emerging model organism for primate biology, behavior, health and conservation. Although much has been learned about their physiology and their Madagascar ecology and phylogeny, little is known about their cellular and molecular biology. Here we used droplet- and plate-based single cell RNA-sequencing to profile 226,000 cells from 27 mouse lemur organs and tissues opportunistically procured from four donors clinically and histologically characterized. Using computational cell clustering, integration, and expert cell annotation, we defined and biologically organized over 750 mouse lemur molecular cell types and their full gene expression profiles. These include cognates of most classical human cell types, including stem and progenitor cells, and the developmental programs for spermatogenesis, hematopoiesis, and other adult tissues. We also described dozens of previously unidentified or sparsely characterized cell types and subtypes. We globally compared cell type expression profiles to define the molecular relationships of cell types across the body, and explored primate cell and gene expression evolution by comparing mouse lemur cell transcriptomes to those of human, mouse, and macaque. This revealed cell type specific patterns of primate specialization, as well as many cell types and genes for which lemur provides a better human model than mouse. The atlas provides a cellular and molecular foundation for studying this primate model organism, and establishes a general approach for other emerging model organisms.

genetics↗
bioRxiv2021

Polygenic enrichment distinguishes disease associations of individual cells in single-cell RNA-seq data

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution without the need for annotation of individual cells to cell types; scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =346K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states; subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region; and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes.

bioinformatics↗
bioRxiv2021

scAgeCom: a murine atlas of age-related changes in intercellular communication inferred with the package scDiffCom

Dysregulation of intercellular communication is a well-established hallmark of aging. To better understand how this process contributes to the aging phenotype, we built scAgeCom, a comprehensive atlas presenting how cell-type to cell-type interactions vary with age in 23 mouse tissues. We first created an R package, scDiffCom, designed to perform differential intercellular communication analysis between two conditions of interest in any mouse or human single-cell RNA-seq dataset. The package relies on its own list of curated ligand-receptor interactions compiled from seven established studies. We applied this tool to single-cell transcriptomics data from the Tabula Muris Senis consortium and the Calico murine aging cell atlas. All the results can be accessed online, using a user-friendly, interactive web application (https://scagecom.org). The most widespread changes we observed include upregulation of immune system processes, inflammation and lipid metabolism, and downregulation of extracellular matrix organization, growth, development and angiogenesis. More specific interpretations are also provided.

bioinformatics↗
bioRxiv2021

cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices

Quickly and flexibly exploring high-dimensional datasets, such as scRNAseq data, is underserved but critical for hypothesis generation, dataset annotation, publication, sharing, and community reuse. cellxgene is a highly generalizable, web-based interface for exploring high dimensional datasets along categorical, continuous and spatial dimensions, as well as feature annotation. cellxgene is differentiated by its ability to performantly handle millions of observations, and bridges a critical gap by enabling computational and experimental biologists to iteratively ask questions of private and public datasets. In doing so, cellxgene increases the utility and reusability of datasets across the single-cell ecosystem. The codebase can be accessed at https://github.com/chanzuckerberg/cellxgene. For questions and inquiries, please contact cellxgene@chanzuckerberg.com.

systems biology↗
bioRxiv2020

Molecular hallmarks of heterochronic parabiosis at single cell resolution

Slowing or reversing biological ageing would have major implications for mitigating disease risk and maintaining vitality. While an increasing number of interventions show promise for rejuvenation, the effectiveness on disparate cell types across the body and the molecular pathways susceptible to rejuvenation remain largely unexplored. We performed single-cell RNA-sequencing on 13 organs to reveal cell type specific responses to young or aged blood in heterochronic parabiosis. Adipose mesenchymal stromal cells, hematopoietic stem cells, hepatocytes, and endothelial cells from multiple tissues appear especially responsive. On the pathway level, young blood invokes novel gene sets in addition to reversing established ageing patterns, with the global rescue of genes encoding electron transport chain subunits pinpointing a prominent role of mitochondrial function in parabiosis-mediated rejuvenation. Intriguingly, we observed an almost universal loss of gene expression with age that is largely mimicked by parabiosis: aged blood reduces global gene expression, and young blood restores it. Altogether, these data lay the groundwork for a systemic understanding of the interplay between blood-borne factors and cellular integrity.

genomics↗