Angela Oliveira Pisco

The paper shows that chemotherapy upregulates genes linked to poor prognosis in surviving leukemia cells, suggesting treatment may increase malignancy rather than reduce it.

The paper benchmarks large language models for cell-type and gene set annotation using AnnDictionary, finding high accuracy in cell-type annotation and strong functional gene set recovery.

This study uses single-cell RNA sequencing to show that M. tuberculosis ESX-1 promotes the recruitment and differentiation of anti-inflammatory macrophages in infected mouse lungs.

The paper identifies 22 molecularly distinct neuronal subtypes in the lateral septum by profiling transcriptional identity and examines their connectivity, morphology, and electrophysiology.

The paper uses deep learning on images of iPSC-derived motor neurons with fALS mutations to identify disease-relevant phenotypes and molecular differences.

This study uses single-nucleus RNA sequencing to analyze a mouse glioblastoma model, identifying potential therapeutic targets and similarities with human GBM subtypes.

The paper shows that intrinsic developmental origins and local signals shape septal astrocyte identity through distinct molecular and morphological features in medial and lateral septal nuclei.

popV improves cell-type annotation by providing uncertainty scores and reducing manual inspection through ensemble voting.

The paper characterizes the transcriptional response to COVID-19 across multiple organs using single-nuclei RNA sequencing, identifying shared transcriptional signatures in endothelial cells and macrophages.

The paper provides a framework and interactive software tools for manually annotating cell types in single-cell multi-omics datasets, demonstrating how different modalities can be combined to improve annotation accuracy.

The paper presents an aging Drosophila cell atlas, identifies cell type-specific aging patterns, and shows ribosomal gene expression as a conserved aging predictor.

Zebrafish embryos sequenced individually across ten developmental stages, paired with light-sheet lineage reconstruction, form an atlas and browser for in silico fate mapping.

The paper compares MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing in mouse liver and kidney, finding MERFISH provides comparable results and robust cell type identification without needing integration with scRNA-seq atlases.

This paper introduces scDRS, a method to identify disease-associated cells in scRNA-seq data using polygenic risk scores, finding novel subpopulations linked to diseases like IBD and schizophrenia.

This paper uses a mouse lemur transcriptomic atlas to identify new genes, splice junctions, and disease-related features, highlighting their relevance to primate biology and human disease.

Nearly 500,000 cells from 24 human tissues, many from one donor, characterize over 400 cell types and enable within-donor study of T-cell clones, B-cell mutation rates and splicing.

A NeurIPS 2021 benchmarking competition evaluated over 2600 submissions from 280 competitors across three multimodal single-cell integration tasks using a purpose-built benchmarking dataset and cloud evaluation pipeline.

The paper creates a comprehensive cell atlas of mouse lemurs using single-cell RNA sequencing, identifying over 750 cell types and comparing their molecular profiles to humans, mice, and macaques.

The paper introduces scAgeCom, an atlas of age-related intercellular communication changes in 23 mouse tissues, using the scDiffCom package to analyze single-cell RNA-seq data from two consortia.

cellxgene couples a Python server to a browser-based JavaScript client, letting researchers cross-filter, re-embed, annotate, and test differential expression in datasets of millions of cells.

MARS identifies and annotates known and novel cell types in single-cell RNA-seq data by transferring latent representations across datasets and generating interpretable names for new cell types.

The paper introduces scRFE, a method using random forests and recursive feature elimination to identify minimal gene sets distinguishing single-cell types in scRNA-seq data.

The paper analyzes single-cell mRNA counts from adult mouse tissues to show that gene expression is often intermittent rather than restricted to specific cell types, with peak transcription levels consistent across tissues but varying probabilities of activity.

OnClass classifies single-cell data into Cell Ontology terms and identifies marker genes for all categories, even those absent in training data.

The paper analyzes the Tabula Muris Senis dataset to identify aging-related gene expression changes across 76 tissue-cell types in mice, revealing both shared and cell-type specific aging signatures.

The paper analyzes aging dynamics across 17 organs and plasma proteomics in mice, revealing consistent gene expression trajectories with organ-specific timing and amplitude.

The paper shows fibroblast state switching between proliferation and ECM deposition defines dermal architecture and is critical for wound healing.

In suspended human keratinocytes, five induced phosphatases dampen ERK to drive differentiation commitment, opposed by DUSP10, in a time-rewired network that renders commitment an unstable switch.