Single-Cell RNA Sequencing Reveals That Adaptation of Human Aortic Endothelial Cells to Antiproliferative Therapies Is Modulated by Flow-Induced Shear Stress.

BACKGROUND: Endothelial cells (ECs) are capable of quickly responding in a coordinated manner to a wide array of stresses to maintain vascular homeostasis. Loss of EC cellular adaptation may be a potential marker for cardiovascular disease and a predictor of poor response to endovascular pharmacological interventions such as drug-eluting stents. Here, we report single-cell transcriptional profiling of ECs exposed to multiple stimulus classes to evaluate EC adaptation. METHODS: Human aortic ECs were costimulated with both pathophysiological flows mimicking shear stress levels found in the human aorta (laminar and turbulent, ranging from 2.5 to 30 dynes/cm2) and clinically relevant antiproliferative drugs, namely paclitaxel and rapamycin. EC state in response to these stimuli was defined using single-cell RNA sequencing. RESULTS: We identified differentially expressed genes and inferred the TF (transcription factor) landscape modulated by flow shear stress using single-cell RNA sequencing. These flow-sensitive markers differentiated previously identified spatially distinct subpopulations of ECs in the murine aorta. Moreover, distinct transcriptional modules defined flow- and drug-responsive EC adaptation singly and in combination. Flow shear stress was the dominant driver of EC state, altering their response to pharmacological therapies. CONCLUSIONS: We showed that flow shear stress modulates the cellular capacity of ECs to respond to paclitaxel and rapamycin administration, suggesting that while responding to different flow patterns, ECs experience an impairment in their transcriptional adaptation to other stimuli.

LAT encodes T cell activation pathway balance.

Immune cells transduce environmental stimuli into responses essential for host health via complex signaling cascades. T cells, in particular, leverage their unique T cell receptors (TCRs) to detect specific Human Leukocyte Antigen (HLA)-presented peptides. TCR activation is then relayed via linker for activation of T cells (LAT), a TCR-proximal disordered adapter protein, which organizes protein partners and mediates the propagation of signals down diverse pathways including NFAT and AP-1. Here, we studied how balanced downstream pathway activation is encoded in the amino acid sequence of LAT. To comprehensively profile the sequence-function relationship of LAT, we developed a pooled, single-cell, high-content screening approach in which a large series of mutants in the LAT protein were analyzed to characterize their effects on T cell activation. Measuring epigenetic, transcriptomic, and cell surface protein dynamics of single cells harboring distinct LAT mutants, we found functional regions spanning over 40% of the LAT amino acid sequence. Conserved sequence motifs for protein interactions along with charge distribution are critical sequence features, and contribute to interpretation of human genetic variation in LAT. While mutant defect severity spans from moderate to complete loss of function, nearly all defective mutants, irrespective of their position in LAT, confer balanced defects across all downstream pathways. To understand the molecular basis for this observation, we performed proximal protein labeling which demonstrated that disruption of LAT interaction with a single partner protein indirectly disrupts other partner interactions, likely through the dual roles of these proteins as effectors of downstream pathways and bridging factors between LAT molecules. Overall, we report widely distributed functional regions throughout a disordered adapter and a precise physical organization of LAT and interacting molecules which constrains signaling outputs. More broadly, we describe an approach for interrogating sequence-function relationships for proteins with complex activities across regulatory layers of the cell.

Scalable, compressed phenotypic screening using pooled perturbations.

High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.

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.