GET: a foundation model of transcription across human cell types.

Transcriptional regulation, involving the complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate in unseen cell types and conditions. Here, we introduce GET, an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types. GET showcases remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovering universal and cell type specific transcription factor interaction networks. We evaluated its performance on prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors. Specifically, we show GET outperforms current models in predicting lentivirus-based massive parallel reporter assay readout with reduced input data. In fetal erythroblasts, we identify distal (>1Mbp) regulatory regions that were missed by previous models. In B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukemia-risk predisposing germline mutation. In sum, we provide a generalizable and accurate model for transcription together with catalogs of gene regulation and transcription factor interactions, all with cell type specificity.

The Influence of Thermal Parameters on the Self-Nucleation Behavior of Polyphenylene Sulfide (PPS) during Secondary Thermoforming.

During the secondary thermoforming of carbon fiber-reinforced polyphenylene sulfide (CF/PPS) composites, a vital material for the aerospace field, varied thermal parameters profoundly influence the crystallization behavior of the PPS matrix. Notably, PPS exhibits a distinctive self-nucleation (SN) behavior during repeated thermal cycles. This behavior not only affects its crystallization but also impacts the processing and mechanical properties of PPS and CF/PPS composites. In this article, the effects of various parameters on the SN and non-isothermal crystallization behavior of PPS during two thermal cycles were systematically investigated by differential scanning calorimetry. It was found that the SN behavior was not affected by the cooling rate in the second thermal cycle. Furthermore, the lamellar annealing resulting from the heating process in both thermal cycles affected the temperature range for forming the special SN domain, because of the refined lamellar structure, and expelled various defects. Finally, this study indicated that to control the strong melt memory effect in the first thermal cycle, both the heating rate and processing melt temperature need to be controlled simultaneously. This work reveals that through collaborative control of these parameters, the crystalline morphology, crystallization temperature and crystallization rate in two thermal cycles are controlled. Furthermore, it presents a new perspective for controlling the crystallization behavior of the thermoplastic composite matrix during the secondary thermoforming process.

Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data.

Spatial omics technologies can help identify spatially organized biological processes, but existing computational approaches often overlook structural dependencies in the data. Here, we introduce Smoother, a unified framework that integrates positional information into non-spatial models via modular priors and losses. In simulated and real datasets, Smoother enables accurate data imputation, cell-type deconvolution, and dimensionality reduction with remarkable efficiency. In colorectal cancer, Smoother-guided deconvolution reveals plasma cell and fibroblast subtype localizations linked to tumor microenvironment restructuring. Additionally, joint modeling of spatial and single-cell human prostate data with Smoother allows for spatial mapping of reference populations with significantly reduced ambiguity.

Single-cell multi-omics defines the cell-type-specific impact of splicing aberrations in human hematopoietic clonal outgrowths.

RNA splicing factors are recurrently mutated in clonal blood disorders, but the impact of dysregulated splicing in hematopoiesis remains unclear. To overcome technical limitations, we integrated genotyping of transcriptomes (GoT) with long-read single-cell transcriptomics and proteogenomics for single-cell profiling of transcriptomes, surface proteins, somatic mutations, and RNA splicing (GoT-Splice). We applied GoT-Splice to hematopoietic progenitors from myelodysplastic syndrome (MDS) patients with mutations in the core splicing factor SF3B1. SF3B1mut cells were enriched in the megakaryocytic-erythroid lineage, with expansion of SF3B1mut erythroid progenitor cells. We uncovered distinct cryptic 3' splice site usage in different progenitor populations and stage-specific aberrant splicing during erythroid differentiation. Profiling SF3B1-mutated clonal hematopoiesis samples revealed that erythroid bias and cell-type-specific cryptic 3' splice site usage in SF3B1mut cells precede overt MDS. Collectively, GoT-Splice defines the cell-type-specific impact of somatic mutations on RNA splicing, from early clonal outgrowths to overt neoplasia, directly in human samples.

Immunotherapy for breast cancer using EpCAM aptamer tumor-targeted gene knockdown.

New strategies for cancer immunotherapy are needed since most solid tumors do not respond to current approaches. Here we used epithelial cell adhesion molecule EpCAM (a tumor-associated antigen highly expressed on common epithelial cancers and their tumor-initiating cells) aptamer-linked small-interfering RNA chimeras (AsiCs) to knock down genes selectively in EpCAM+ tumors with the goal of making cancers more visible to the immune system. Knockdown of genes that function in multiple steps of cancer immunity was evaluated in aggressive triple-negative and HER2+ orthotopic, metastatic, and genetically engineered mouse breast cancer models. Gene targets were chosen whose knockdown was predicted to promote tumor neoantigen expression (Upf2, Parp1, Apex1), phagocytosis, and antigen presentation (Cd47), reduce checkpoint inhibition (Cd274), or cause tumor cell death (Mcl1). Four of the six AsiC (Upf2, Parp1, Cd47, and Mcl1) potently inhibited tumor growth and boosted tumor-infiltrating immune cell functions. AsiC mixtures were more effective than individual AsiC and could synergize with anti-PD-1 checkpoint inhibition.