Biology subjects

Farr, E.

Publications and source records attributed to Farr, E..

4 recordsLinked to original sources

bioRxiv2026

Function-driven geometry directs human pilosebaceous unit development

Single-cell technologies have generated cell censuses of tissues, however, how tissue geometry reflects functional needs remains poorly characterized. The human pilosebaceous unit offers a tractable model, a prenatally-formed complex mini-organ combining hair and sebum production with a stem cell reservoir. Using histomorphology, spatial transcriptomics, and single-cell multiomics on the same human prenatal scalp skin samples (8-19 post-conception weeks), integrated and analyzed using machine learning approaches, we built a spatiotemporal map of pilosebaceous unit development. We demonstrate that epithelial-mesenchymal interactions coordinate cellular fate and organogenesis, using an in vitro hair-bearing skin organoid model to validate this tissue-patterning. In addition, we show sebaceous gland developmental programmes are overcome during tumor formation. Our large-scale multi-modal analysis provides a unique framework for understanding form and function of tissues with applications in tissue engineering and pathology.

developmental biology↗

bioRxiv2023

LIANA+: an all-in-one cell-cell communication framework

The growing availability of single-cell and spatially-resolved transcriptomics has led to the rapidly growing popularity of methods to infer cell-cell communication. Many approaches have emerged, each capturing only a partial view of the complex landscape of cell-cell communication. Here, we present LIANA+, a scalable framework to decode coordinated inter- and intracellular signalling events from single- and multi-condition datasets in both single-cell and spatially-resolved data. Beyond integrating and extending established methodologies and a rich knowledge base, LIANA+ enables novel analyses using diverse molecular mediators, including those measured in multi-omics data. Accessible as an open-source Python package at https://github.com/saezlab/liana-py, LIANA+ provides a comprehensive set of synergistic components to study cell-cell communication. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/553863v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@16486ceorg.highwire.dtl.DTLVardef@ce6821org.highwire.dtl.DTLVardef@1165e54org.highwire.dtl.DTLVardef@18add5a_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

A single cell atlas of sexual development in Plasmodium falciparum

The developmental decision made by malaria parasites to become sexual underlies all malaria transmission. Here, we describe a rich atlas of short and long-read single-cell transcriptomes of over 37,000 Plasmodium falciparum cells across intraerythrocytic asexual and sexual development. We used the atlas to explore transcriptional modules and exon usage along sexual development, and expanded it to include malaria parasites collected from a Malian individual naturally infected with multiple P. falciparum strains. We investigated genotypic and transcriptional heterogeneity within and among these wild strains at a single-cell level for the first time, finding considerable differential expression between different strains even within the same host. This work is a key addition to the Malaria Cell Atlas, enabling a deeper understanding of the biology and diversity of transmission stages. One sentence summaryThis addition to the Malaria Cell Atlas presents an analysis of sexual development and uses it to explore a natural infection.

genomics↗

bioRxiv2021

SPOT: a web-tool enabling Swift Profiling Of Transcriptomes

The increasing number of single cell and bulk RNAseq data sets describing complex gene expression profiles in different organisms, organs or cell types calls for an intuitive tool allowing rapid comparative analysis. Here we present Swift Profiling Of Transcriptomes (SPOT) as a web tool that allows not only differential expression analysis but also fast ranking of genes fitting transcription profiles of interest. Based on a heuristic approach the spot algorithm ranks the genes according to their proximity to the user-defined gene expression profile of interest. The best hits are visualized as a table, bar chart or dot plot and can be exported as an Excel file. While the tool is generally applicable, we tested it on RNAseq data from malaria parasites that undergo multiple stage transformations during their complex life cycle as well as on data from multiple human organs during development and cell lines infected by the SARS-CoV-2 virus. SPOT should enable non-bioinformaticians to easily analyse their own and any available dataset.

bioinformatics↗