Biology subjects

Mold, J. E.

Publications and source records attributed to Mold, J. E..

12 recordsLinked to original sources

bioRxiv2026

Single-cell proteomics reveals cell-type-specific functional coordination in PBMCs

The coordination of molecular networks defines cellular functions. This is reflected in molecular covariation within a cell type, which is more subtle than the differences separating cell types and has therefore been difficult to quantify. To achieve the depth, consistency and accuracy required to resolve such covariation, we leveraged single-cell proteomics (plexDIA) and transcriptomics (Smart-seq3xpress) to analyze the proteomes and transcriptomes of thousands of single peripheral blood mononuclear cells (PBMCs). plexDIA quantified more protein-coding gene products per cell with higher data completeness while Smart-seq3xpress quantified more gene products across all cells. The protein measurements revealed cell-type-specific proteome architecture -- shaped in part by protein stability, and complex coordination -- that was undetectable in our mRNA data. Specifically, protein covariation within a cell type suggests cell-type-specific protein-protein interactions and functional rewiring of biological pathways independent of previously characterized abundance differences. Covariation analysis within cell types resolves a B cell-specific axis of translational states inversely covarying with GDF6 cytokine abundance. Our framework captures cell-type-specific functional coordination representing a distinct information layer accessible via single-cell proteomics.

systems biology↗

bioRxiv2025

Multi-cellular phenotypic dynamics during the progression of breast tumors

In cancer, improving diagnostics and therapeutic interventions can benefit from understanding the cellular and phenotypic heterogeneity of the tumor microenvironment (TME). In recent years, the TME has been profiled at an unprecedented level of detail by performing single-cell RNA sequencing (scRNAseq) on patient samples. However, from patient samples, studying the temporal dynamics of the TME using patient samples has been challenging. Interrogating the temporal dynamics of the TME is critical to understand how inter-tumor heterogeneity is organized into a temporally ordered sequence of causes and consequences in cellular events. Here we survey the temporal dynamics of the TME by performing longitudinal scRNAseq on mouse breast tumors at different progression time points of tumor progression. We reveal multi-cellular phenotypic dynamics that follow one out of three possible temporal patterns: stable colonization, wave-like, or progressive increase. In particular, IFN-responsive cancer cells, GzmB + cytotoxic T cells, as well as C1q macrophages, progressively increase in parallel with tumors progression. These findings establish the single-cell types and phenotypes in a progressing breast tumor, and determine when these cellular players enter and leave the TME.

cancer biology↗

bioRxiv2025

Cell lineage inference from mitochondrial mutations

Tracing genealogical relationships between cells and their molecular states is essential for understanding tissue development, maintenance and disease. We present mt-SCITE, a computational method that reconstructs cell division histories from mitochondrial mutations in single cells. On simulated and experimental benchmarks, mt-SCITE outperformed existing methods in terms of cell lineage reconstruction, and scaled to thousands of cells. Applied to expanded CD8+ T cells following vaccination, mt-SCITE resolved subclonal relationships and revealed clones with predominantly memory-like, effector-like, or mixed states. mt-SCITE robustly identifies clonally related populations and resolves substructure within them, providing a broadly applicable approach to study cell lineage development.

bioinformatics↗

bioRxiv2024

Distinct tissue-dependent composition and gene expression of human fetal innate lymphoid cells

The human fetal immune system starts to develop in the first trimester and likely plays a crucial role in fetal development and maternal-fetal tolerance. Innate lymphoid cells (ILCs) are the earliest lymphoid cells to arise in the human fetus. ILCs consists of natural killer (NK) cells, ILC1s, ILC2s, and ILC3s that all share a common lymphoid origin. Here, we studied fetal ILC subsets, mainly NK cells and ILC3s and their potential progenitors, across human fetal tissues from the first and second trimesters. Our results show that fetal ILC subsets have distinct distribution, developmental kinetics, and gene expression profiles across human fetal tissues. Furthermore, we identify a putative CD34+ROR{gamma}t+Eomes+/- ILC progenitor population exclusively present in fetal intestine, indicating that tissue-restricted development of ILCs could contribute to the variation in ILC composition and gene expression between tissues.

immunology↗

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

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

Long-read whole genome analysis of human single cells

With long-read sequencing, we have entered an era where individual genomes are routinely assembled to near completion and where complex genetic variation can efficiently be resolved. Here, we demonstrate that long reads can be applied to study the genomic architecture of individual human cells. Clonally expanded CD8+ T-cells from a human donor were used as starting material for a droplet-based multiple displacement amplification (dMDA) to generate long molecules with minimal amplification bias. PacBio HiFi sequencing generated up to 20 Gb data and 40% genome coverage per single cell. The data allowed for accurate detection and haplotype phasing of single nucleotide variants (SNVs), structural variants (SVs), and tandem repeats, including in genomic regions inaccessible by short reads. Somatic SNVs were detected in the nuclear genome and mitochondrial DNA. An average of 1278 high-confidence SVs per cell were discovered in the PacBio data, nearly four times as many compared to those found in Illumina dMDA data from clonally related cells. Single-cell de novo assembly resulted in a genome size of up to 598 Mb and 1762 (12.8%) complete gene models. In summary, the work presented here demonstrates the utility of whole genome amplification combined with long-read sequencing toward the characterization of the full spectrum of genetic variation at the single-cell level.

genomics↗

bioRxiv2020

Activation of a neural stem cell transcriptional program in parenchymal astrocytes

Neural stem cells, located in discrete niches in the adult brain, generate new neurons throughout life. These stem cells are specialized astrocytes, but astrocytes in other brain regions do not generate neurons under physiological conditions. After stroke, however, striatal astrocytes undergo neurogenesis in mice, triggered by decreased Notch signaling. We used single-cell RNA sequencing to characterize neurogenesis by Notch-depleted striatal astrocytes in vivo. Striatal astrocytes were located upstream of neural stem cells in the neuronal lineage. As astrocytes initiated neurogenesis, they became transcriptionally very similar to subventricular zone stem cells and progressed through a nearly identical neurogenic program. Surprisingly, in the non- neurogenic cortex, Notch-depleted astrocytes also initiated neurogenesis. Yet, the cortical astrocytes, and many striatal ones, stalled before entering transit- amplifying divisions. Infusion of epidermal growth factor enabled stalled striatal astrocytes to resume neurogenesis. We conclude that parenchymal astrocytes are latent neural stem cells and that targeted interventions can guide them through their neuronal differentiation.

neuroscience↗

bioRxiv2019

High-dimensional analysis reveals a distinct population of adaptive-like tissue-resident NK cells in human lung

Human adaptive-like “memory” CD56dimCD16+ NK cells in peripheral blood from cytomegalovirus-seropositive individuals have been extensively investigated in recent years and are currently explored as a treatment strategy for hematological cancers. However, treatment of solid tumors remains limited due to insufficient NK cell tumor infiltration, and it is unknown whether large expansions of adaptive-like NK cells that are equipped for tissue-residency and tumor-homing exist in peripheral tissues. Here, we show that human lung and blood contains adaptive-like CD56brightCD16− NK cells with hallmarks of tissue-residency, including expression of CD49a. Expansions of adaptive-like lung tissue-resident (tr)NK cells were found to be present independently of adaptive-like CD56dimCD16+ NK cells and to be hyperresponsive towards target cells. Together, our data demonstrate that phenotypically, functionally, and developmentally distinct subsets of adaptive-like NK cells exist in human lung and blood. Given their tissue-related character and hyperresponsiveness, human lung adaptive-like trNK cells might represent a suitable alternative for therapies targeting solid tumors.Competing Interest StatementThe authors have declared no competing interest.Abbreviations:EomesEomesoderminFITCFluorescein isothiocyanateHCMVHuman cytomegalovirusILCInnate lymphoid cellKIRKiller cell immunoglobulin-like receptorNKNatural killerPEPhycoerythrinROSReactive oxygen speciesView Full Text

immunology↗

bioRxiv2019

Clonally distinct differentiation trajectories shape CD8+ memory T cell heterogeneity after acute viral infections in humans

CD8+ T cells play essential roles in immunity to viral and bacterial infections, and to guard against malignant cells. The CD8+ T cell response to an antigen is composed of many T cell clones with unique T cell receptors, together forming a heterogenous repertoire of phenotypically and functionally distinct effector and memory cells1, 2. How individual T cell clones contribute to this heterogeneity during an immune response is key to understand immunity but remains largely unknown. Here, we longitudinally tracked CD8+ T cell clones expanding in response to yellow fever virus vaccination at the single cell level in humans. We show that only a fraction of the clones detected in the acute response persists as circulating memory T cells, indicative of clonal selection. Clones persisting in the memory phase displayed biased differentiation trajectories along a gradient of stem cell memory (SCM) towards terminally differentiated effector memory (EMRA) fates. Reactivation of single memory CD8+ T cells revealed that they were poised to recapitulate skewed differentiation trajectories in secondary responses, and this was generalizable across individuals for both yellow fever and influenza virus. Together, we show that the sum of distinct clonal differentiation repertoires results in the multifaceted T cell response to acute viral infections in humans.

immunology↗

bioRxiv2018

Conbase: a software for discovery of clonal somatic mutations in single cells through read phasing

Here we report the development of Conbase, a software application for the identification of somatic mutations in single cell DNA sequencing data with high rates of allelic dropout and at low read depth. Conbase leverages data from multiple samples in a dataset and utilizes read phasing to call somatic single nucleotide variants and to accurately predict genotypes in whole genome amplified single cells in somatic variant loci. We demonstrate the accuracy of Conbase on simulated datasets, in vitro expanded fibroblasts and clonally in vivo expanded lymphocyte populations isolated directly from a healthy human donor.

bioinformatics↗

bioRxiv2017

Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis

The development of high throughput single-cell technologies now allows the investigation of the genome-wide diversity of transcription. This diversity has shown two faces: the expression dynamics (gene to gene variability) can be quantified more accurately, thanks to the measurement of lowly-expressed genes. Second, the cell-to-cell variability is high, with a low proportion of cells expressing the same gene at the same time/level. Those emerging patterns appear to be very challenging from the statistical point of view, especially to represent and to provide a summarized view of single-cell expression data. PCA is one of the most powerful frameworks to provide a suitable representation of high dimensional datasets, by searching for new axes catching the most variability in the data. Unfortunately, classical PCA is based on Euclidean distances and projections that work poorly in presence of over-dispersed counts showing zero-inflation. We propose a probabilistic Count Matrix Factorization (pCMF) approach for single-cell expression data analysis, that relies on a sparse Gamma-Poisson factor model. This hierarchical model is inferred using a variational EM algorithm. We show how this probabilistic framework induces a geometry that is suitable for single-cell data, and produces a compression of the data that is very powerful for clustering purposes. Our method is competed to other standard representation methods like ZIFA and t-SNE, and we illustrate its performance on simulated and publicly available data.

bioinformatics↗