Biology subjects

Ramseier, M. L.

Publications and source records attributed to Ramseier, M. L..

5 recordsLinked to original sources

bioRxiv2024

Mutation and cell state compatibility is required and targetable in Ph+ acute lymphoblastic leukemia minimal residual disease

Efforts to cure BCR::ABL1 B cell acute lymphoblastic leukemia (Ph+ ALL) solely through inhibition of ABL1 kinase activity have thus far been insufficient despite the availability of tyrosine kinase inhibitors (TKIs) with broad activity against resistance mutants. The mechanisms that drive persistence within minimal residual disease (MRD) remain poorly understood and therefore untargeted. Utilizing 13 patient-derived xenograft (PDX) models and clinical trial specimens of Ph+ ALL, we examined how genetic and transcriptional features co-evolve to drive progression during prolonged TKI response. Our work reveals a landscape of cooperative mutational and transcriptional escape mechanisms that differ from those causing resistance to first generation TKIs. By analyzing MRD during remission, we show that the same resistance mutation can either increase or decrease cellular fitness depending on transcriptional state. We further demonstrate that directly targeting transcriptional state-associated vulnerabilities at MRD can overcome BCR::ABL1 independence, suggesting a new paradigm for rationally eradicating MRD prior to relapse. Finally, we illustrate how cell mass measurements of leukemia cells can be used to rapidly monitor dominant transcriptional features of Ph+ ALL to help rationally guide therapeutic selection from low-input samples. HIGHLIGHTSO_LIRelapse after remission on TKI can harbor mutations in ABL1, RAS, or neither C_LIO_LIMutations and development-like cell state dictate fitness in residual disease C_LIO_LICo-targeting cell state and ABL1 markedly reduces MRD C_LIO_LIBiophysical measurements provide an integrative, rapid measurement of cell state C_LI

cancer biology↗

bioRxiv2024

A knockoff calibration method to avoid over-clustering in single-cell RNA-sequencing

Standard single-cell RNA-sequencing (scRNA-seq) pipelines nearly always include unsupervised clustering as a key step in identifying biologically distinct cell types. A follow-up step in these pipelines is to test for differential expression between the identified clusters. When algorithms over-cluster, downstream analyses will produce inflated P -values resulting in increased false discoveries. In this work, we present callback (Calibrated Clustering via Knockoffs): a new method for protecting against over-clustering by controlling for the impact of reusing the same data twice when performing differential expression analysis, commonly known as "double-dipping". Importantly, our approach can be applied to a wide range of clustering algorithms. Using real and simulated data, we show that callback provides state-of-the-art clustering performance and can rapidly analyze large-scale scRNA-seq studies, even on a personal laptop.

bioinformatics↗

bioRxiv2024

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data

Clustering is commonly used in single-cell RNA-sequencing (scRNA-seq) pipelines to characterize cellular heterogeneity. However, current methods face two main limitations. First, they require user-specified heuristics which add time and complexity to bioinformatic workflows; second, they rely on post-selective differential expression analyses to identify marker genes driving cluster differences, which has been shown to be subject to inflated false discovery rates. We address these challenges by introducing nonparametric clustering of single-cell populations (NCLUSION): an infinite mixture model that leverages Bayesian sparse priors to identify marker genes while simultaneously performing clustering on single-cell expression data. NCLUSION uses a scalable variational inference algorithm to perform these analyses on datasets with up to millions of cells. Through simulations and analyses of publicly available scRNA-seq studies, we demonstrate that NCLUSION (i) matches the performance of other state-of-the-art clustering techniques with significantly reduced runtime and (ii) provides statistically robust and biologically relevant transcriptomic signatures for each of the clusters it identifies. Overall, NCLUSION represents a reliable hypothesis-generating tool for understanding patterns of expression variation present in single-cell populations.

bioinformatics↗

bioRxiv2024

Congenital T cell activation impairs transitional to follicular B cell maturation in humans

CTLA4-deficient patients exhibit profound humoral immune dysfunction, yet the basis for the B cell defect is not known. We observed a marked reduction in transitional to follicular B cell development in CTLA4-deficient patients, correlating with decreased CTLA4 function in regulatory T cells and increased mTORC1 signaling in transitional B cells. Treatment of transitional B cells with CD40L was sufficient to induce mTORC1 signaling and inhibit follicular B cell maturation in vitro. Frequent cell-cell contacts between CD40L+ T cells and naive IgD+CD27- B cells were observed in patient lymph nodes. Follicular B cell maturation in CTLA-deficient patients was partially rescued following CTLA4 replacement therapy in vivo. We conclude that functional regulatory T cells and the containment of excessive T cell activation are required for follicular B cells to mature and attain metabolic quiescence and thus acquire a state of immunological self-tolerance. One Sentence SummaryCongenital T cell activation in CTLA4-deficient patients impairs transitional to follicular B cell maturation and can be rescued by CTLA4 replacement therapy in vivo.

immunology↗

bioRxiv2023

Chronic metabolic stress drives developmental programs and loss of tissue functions in non-transformed liver that mirror tumor states and stratify survival

Under chronic stress, cells must balance competing demands between cellular survival and tissue function. In metabolic dysfunction-associated steatotic liver disease (MASLD, formerly NAFLD/NASH), hepatocytes cooperate with structural and immune cells to perform crucial metabolic, synthetic, and detoxification functions despite nutrient imbalances. While prior work has emphasized stress-induced drivers of cell death, the dynamic adaptations of surviving cells and their functional repercussions remain unclear. Namely, we do not know which pathways and programs define cellular responses, what regulatory factors mediate (mal)adaptations, and how this aberrant activity connects to tissue-scale dysfunction and long-term disease outcomes. Here, by applying longitudinal single-cell multi-omics to a mouse model of chronic metabolic stress and extending to human cohorts, we show that stress drives survival-linked tradeoffs and metabolic rewiring, manifesting as shifts towards development-associated states in non-transformed hepatocytes with accompanying decreases in their professional functionality. Diet-induced adaptations occur significantly prior to tumorigenesis but parallel tumorigenesis-induced phenotypes and predict worsened human cancer survival. Through the development of a multi-omic computational gene regulatory inference framework and human in vitro and mouse in vivo genetic perturbations, we validate transcriptional (RELB, SOX4) and metabolic (HMGCS2) mediators that co-regulate and couple the balance between developmental state and hepatocyte functional identity programming. Our work defines cellular features of liver adaptation to chronic stress as well as their links to long-term disease outcomes and cancer hallmarks, unifying diverse axes of cellular dysfunction around core causal mechanisms.

systems biology↗