Biology subjects

Toosi, H.

Publications and source records attributed to Toosi, H..

7 recordsLinked to original sources

bioRxiv2026

DisCell: Separating Spillover from Niche Response in Spatial Transcriptomics

Spatial transcriptomics at single-cell resolution measures every cell in its place in the tissue, but diffusion and segmentation errors also add transcripts of neighboring cells to a cell's counts. This spillover makes a cell resemble its neighbors, and a response to the niche makes cells in the same niche resemble each other. Both tie a cell's expression to its surroundings. The count data alone cannot tell the two apart. Findings from single-cell transcriptomes, such as a gene program that is higher in one niche than in another or signaling genes that change depending on the neighborhood, may therefore be artifacts of spillover. Current models describe a cell by its intrinsic state together with either its niche response or spillover, but none models all three. We present DisCell, the first method that models all three components jointly and measures how robust each finding is to spillover. DisCell is a generative model that describes single-cell gene expression profiles in terms of two latent variables, an intrinsic state and a response to the niche, and adds spillover from its neighbors as a separate term. DisCell fits the model at increasing levels of spillover and recomputes each finding on the spillover-free expression to see how much spillover it withstands. DisCell also estimates the most spillover a section can contain, and a finding that holds even at that amount is not explained by spillover. In simulated tissue, DisCell recovers each cell's intrinsic state and response and tells findings created by spillover apart from genuine responses. On five sections of ovarian and lung tissue, the intrinsic state keeps more of each cell's own properties, measured by its cell-cycle phase, than any competing method that holds as little of its neighborhood. Within each cell type, the model partitions the variation of each gene into intrinsic state, niche response and spillover. Using DisCell, we rank findings about niches by their robustness to spillover and confirm that a previously reported finding withstands spillover, that esophageal cancer cells in niches infiltrated by immune or stromal cells express more of an epithelial-mesenchymal transition program. DisCell also predicts what the gene expression of a cell type would be in another niche, with its spillover part separated, at lower error on most tasks than the competing method built for this purpose. Training the model takes under ten minutes on one consumer GPU for sections of up to 400,000 cells.

bioinformatics↗

bioRxiv2026

Pubertal maturation and chemotherapy-associated disruption of the pediatric ovary revealed by multimodal single-cell profiling

Ovarian tissue cryopreservation enables fertility preservation in females undergoing gonadotoxic therapies, restoring fertility in adults. Although offered even before puberty, the childhood ovary and its vulnerability to therapy remain poorly characterized. Here, ovarian tissue from 16 patients undergoing fertility preservation (aged 1-16 years) and 11 adult controls (aged 22-32 years) was analyzed using single-cell RNA sequencing, spatial transcriptomics, and multiplex immunostaining. In chemotherapy-naive samples, 13 somatic cell populations underwent extracellular matrix remodeling, vascular, neural, and stromal maturation during puberty, whereas changes in germline related to chromatin remodeling. Spatial transcriptomics resolved 23 clusters across, revealing distinct tissue organization and follicular niche composition between children and adults. Chemotherapy exposure depleted perifollicular and vascular cells, suppressed intercellular signaling, and dysregulated over half of puberty-associated genes, converging on stress responses and extracellular matrix remodeling, with SEPTIN7 as a potential biomarker. These findings uncover critical developmental vulnerabilities of the pediatric ovary relevant to fertility preservation.

developmental biology↗

bioRxiv2022

Spatial transcriptomics of T and B cell receptors uncovers lymphocyte clonal dynamics in human tissue.

The spatial distribution of lymphocyte clones within tissues is critical to their development, selection, and expansion. We have developed Spatial Transcriptomics of VDJ sequences (Spatial VDJ), which maps immunoglobulin and TR antigen receptors in human tissue sections. Spatial VDJ captures lymphocyte clones matching canonical T, B, and plasma cell distributions in tissues and amplifies clonal sequences confirmed by orthogonal methods. We confirm spatial congruency between paired receptor chains, develop a computational framework to predict receptor pairs, and link the expansion of distinct B cell clones to different tumor-associated gene expression programs. Spatial VDJ delineates B cell clonal diversity, class switch recombination, and lineage trajectories within their spatial context. Taken together, Spatial VDJ captures lymphocyte spatial clonal architecture across tissues, which could have important therapeutic implications. One-Sentence SummarySpatial transcriptomics-based technology co-captures T and B cell receptors within their anatomical niche in human tissue.

immunology↗

bioRxiv2022

Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues

Spatial and genomic heterogeneity of tumors is the key for cancer progression, treatment, and survival. However, a technology for direct mapping the clones in the tumor tissue based on point mutations is lacking. Here, we propose Tumoroscope, the first probabilistic model that accurately infers cancer clones and their high-resolution localization by integrating pathological images, whole exome sequencing, and spatial transcriptomics data. In contrast to previous methods, Tumoroscope explicitly addresses the problem of deconvoluting the proportions of clones in spatial transcriptomics spots. Applied to a reference prostate cancer dataset and a newly generated breast cancer dataset, Tumoroscope reveals spatial patterns of clone colocalization and mutual exclusion in sub-areas of the tumor tissue. We further infer clone-specific gene expression levels and the most highly expressed genes for each clone. In summary, Tumoroscope enables an integrated study of the spatial, genomic, and phenotypic organization of tumors.

cancer biology↗

bioRxiv2022

Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperformed other methods on simulated data, successfully indicated known brain structures and spatially distinguished between inhibitory and excitatory neuron types based in mouse brain tissue, and dissected large heterogeneity of immune infiltrate composition in prostate gland tissue.

bioinformatics↗

bioRxiv2022

Clonally heritable gene expression imparts a layer of diversity within cell types

Cell types can be classified based on shared patterns of transcription. Variability in gene expression between individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. We asked whether long-term, heritable differences in transcription can impart diversity within a cell type. Studying clonal human lymphocytes and mouse brain cells, we uncover a vast diversity of heritable transcriptional states among different clones of cells of the same type in vivo. In lymphocytes we show that this diversity is coupled to clone specific chromatin accessibility, resulting in distinct expression of genes by different clones. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging and disease.

systems biology↗

bioRxiv2021

PhylEx: Accurate reconstruction of clonal structure via integrated analysis of bulk DNA-seq and single cell RNA-seq data

We propose PhylEx: a clonal-tree reconstruction method that integrates bulk genomics and single-cell transcriptomics data. In addition to the clonal-tree, PhylEx also assigns single-cells to clones, which effectively produce clonal expression profiles, and generates clonal genotypes. By analyzing scRNA-seq integrated with bulk DNA-seq, PhylEx can take advantage of co-occurrences of the mutations found in the cells. In the probabilistic model underlying PhylEx, the raw read counts from scRNA-seq follow a mixture of Beta-Binomial distributions, which accounts for the sparse nature of single-cell gene expression data; the mixture lessens the penalty caused by mutations not observed due to mono-allelic expression. We rigorously evaluated PhylEx on simulated datasets as well as a biological dataset consisting of a previously well-characterized high-grade serous ovarian cancer (HGSOC) cell line. PhylEx outperformed the state-of-the-art methods by a wide margin both when comparing capacity for clonal-tree reconstruction and capacity for correctly clustering mutations. By analyzing HGSOC and HER2+ breast cancer data, we also show that PhylEx clears the way for phylo-phenotypic analysis of cancer, i.e., that the clonal expression profiles, induced by the cell-to-clone assignments, can be exploited in a manner beyond what is possible with only expression-based clustering.

bioinformatics↗