Spatial transcriptomics reveals substantial heterogeneity in triple-negative breast cancer with potential clinical implications.

While triple-negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigate TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We perform an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics.

Standalone single- and bi-layered human skin 3D models supported by recombinant silk feature native spatial organization.

Physiologically relevant human skin models that include key skin cell types can be used fordrug testing, skin pathology studies, or clinical applications such as skin grafts. However, there is still no golden standard for such a model. We investigated the potential of a recombinant functionalized spider silk protein, FN-silk, for the construction of a dermal, an epidermal, and a bilayered skin equivalent (BSE).

Lokatt: a hybrid DNA nanopore basecaller with an explicit duration hidden Markov model and a residual LSTM network.

Background: Basecalling long DNA sequences is a crucial step in nanopore-based DNA sequencing protocols. In recent years, the CTC-RNN model has become the leading basecalling model, supplanting preceding hidden Markov models (HMMs) that relied on pre-segmenting ion current measurements. However, the CTC-RNN model operates independently of prior biological and physical insights.

Spatial transcriptomics of human placentas reveal distinct RNA patterns associated with morphology and preeclampsia.

Spatial transcriptomics (ST) maps RNA level patterns within a tissue. This technology has not been previously applied to human placental tissue. We demonstrate analysis of human placental samples with ST.

An integrated single cell and spatial transcriptomic map of human white adipose tissue.

To date, single-cell studies of human white adipose tissue (WAT) have been based on small cohort sizes and no cellular consensus nomenclature exists. Herein, we performed a comprehensive meta-analysis of publicly available and newly generated single-cell, single-nucleus, and spatial transcriptomic results from human subcutaneous, omental, and perivascular WAT. Our high-resolution map is built on data from ten studies and allowed us to robustly identify >60 subpopulations of adipocytes, fibroblast and adipogenic progenitors, vascular, and immune cells.

Solid-phase capture and profiling of open chromatin by spatial ATAC.

Current methods for epigenomic profiling are limited in their ability to obtain genome-wide information with spatial resolution. We introduce spatial ATAC, a method that integrates transposase-accessible chromatin profiling in tissue sections with barcoded solid-phase capture to perform spatially resolved epigenomics. We show that spatial ATAC enables the discovery of the regulatory programs underlying spatial gene expression during mouse organogenesis, lineage differentiation and in human pathology.

Spatial mapping reveals human adipocyte subpopulations with distinct sensitivities to insulin.

The contribution of cellular heterogeneity and architecture to white adipose tissue (WAT) function is poorly understood. Herein, we combined spatially resolved transcriptional profiling with single-cell RNA sequencing and image analyses to map human WAT composition and structure. This identified 18 cell classes with unique propensities to form spatially organized homo- and heterotypic clusters.