Biology subjects

Nadel, B.

Publications and source records attributed to Nadel, B..

3 recordsLinked to original sources

bioRxiv2022

Functional plasticity and recurrent cell states of malignant B cells in follicular lymphoma

Follicular lymphoma (FL) derives from malignant transformation of germinal center (GC) B cells. FL malignant B cells are heterogeneous and diverge from their GC B cell-of-origin, but the diversity, function, and location of malignant B cell states remain to be addressed. Based on integrative single-cell RNA-seq, we identified and studied recurrent FL malignant B cell states and dynamics. Most FL B cells spanned a continuum of states from proliferating GC-like to quiescent memory (Mem)-like cell states. That GC-to-Mem axis was the main source of intra-tumor transcriptional heterogeneity. While FL B cell states were independent from subclonal B cell receptor genetics divergence, T follicular helper (TFH) cell-derived signals controlled the transition from Mem-like to GC-like states. GC-like, TFH-activated and Mem-like FL B cells tended to occupy distinct niches within and around tumor follicles. Our study characterizes novel malignant cell states recurrent in B cell lymphomas, and highlights the functional plasticity of malignant B cells.

cancer biology↗

bioRxiv2021

Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples

Estimating cell type composition of blood and tissue samples is a biological challenge relevant in both laboratory studies and clinical care. In recent years, a number of computational tools have been developed to estimate cell type abundance using gene expression data. While these tools use a variety of approaches, they all leverage expression profiles from purified cell types to evaluate the cell type composition within samples. In this study, we compare ten deconvolution tools and evaluate their performance while using each of eleven separate reference profiles. Specifically, we have run deconvolution tools on over 4,000 samples with known cell type proportions, spanning both immune and stromal cell types. Twelve of these represent in vitro synthetic mixtures and 300 represent in silico synthetic mixtures prepared using single cell data. A final 3,728 clinical samples have been collected from the Framingham Cohort, for which cell populations have been quantified using electrical impedance cell counting. When tools are applied to the Framingham dataset, the tool EPIC produces the highest correlation while GEDIT produces the lowest error. The best tool for other datasets is varied, but CIBERSORT and GEDIT most consistently produce accurate results. In terms of reference choice, we find that the Human Primary Cell Atlas (HPCA) and references published by the EPIC authors produce accurate results for the largest number of tools and datasets. When applying deconvolution to blood samples, the leukocyte reference matrix LM22 is also a suitable choice, usually (but not always) outperforming HPCA and EPIC. Running time varies substantially across tools. For as many as 5052 samples, SaVanT and dtangle reliably finish in under one minute, while slower tools may require up to two hours. However, when using custom references, CIBERSORT can run very slowly, taking over 24 hours to complete for large datasets. We conclude that combining the best tools with optimal reference datasets can provide significant gains in accuracy when carrying out deconvolution tasks.

bioinformatics↗

bioRxiv2019

Cell type specific genetic regulation of gene expression across human tissues

The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci (cis-QTLs) for the majority of genes across a wide range of human tissues. However, the interpretation of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. Here, we map interactions between computational estimates of cell type abundance and genotype to identify cell type interaction QTLs for seven cell types and show that cell type interaction eQTLs provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations to 87 complex traits show a contribution from cell type interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.\n\nOne Sentence SummaryEstimated cell type abundances from bulk RNA-seq across tissues reveal the cellular specificity of quantitative trait loci.

genomics↗