E-spatial

In the realm of cancer research, grasping the intricacies of intratumor heterogeneity and its interplay with the immune system is paramount for deciphering treatment resistance and tumor progression. While single-cell RNA sequencing unveils diverse transcriptional programs, the challenge persists in automatically discerning malignant cells from non-malignant ones within complex datasets featuring varying coverage depths. Thus, there arises a compelling need for an automated solution to this classification conundrum. SCEVAN (De Falco et al., 2023), a variational algorithm, is designed to autonomously identify the clonal copy number substructure of tumors using single-cell data. It automatically separates malignant cells from non-malignant ones, and subsequently, groups of malignant cells are examined through an optimization-driven joint segmentation process.

In the realm of transcriptional dynamics, understanding the intricate interplay of regulatory proteins is crucial for deciphering processes ranging from normal development to disease progression. However, traditional RNA velocity methods often overlook the underlying regulatory drivers of gene expression changes over time. This gap in knowledge hinders our ability to unravel the mechanistic intricacies of these dynamic processes. scKINETICs (Key regulatory Interaction NETwork for Inferring Cell Speed) (Burdziak et al, 2023) offers a dynamic model for gene expression changes that simultaneously learns per-cell transcriptional velocities and a governing gene regulatory network. By employing an expectation-maximization approach, scKINETICS quantifies the impact of each regulatory element on its target genes, incorporating insights from epigenetic data, gene-gene coexpression patterns and constraints dictated by the phenotypic manifold.

Computational methods that model how the gene expression of a cell is influenced by interacting cells are lacking. We present NicheNet, a method that predicts ligand–target links between interacting cells by combining their expression data with prior knowledge of signaling and gene regulatory networks. We applied NicheNet to the tumor and immune cell microenvironment data and demonstrated that NicheNet can infer active ligands and their gene regulatory effects on interacting cells.

Single-cell RNA sequencing (scRNA-seq) data have allowed us to investigate cellular heterogeneity and the kinetics of a biological process. Some studies need to understand how cells change state, and corresponding genes during the process, but it is challenging to track the cell development in scRNA-seq protocols. Therefore, a variety of statistical and computational methods have been proposed for lineage inference (or pseudotemporal ordering) to reconstruct the states of cells according to the developmental process from the measured snapshot data. Specifically, lineage refers to an ordered transition of cellular states, where individual cells represent points along. pseudotime is a one-dimensional variable representing each cell’s transcriptional progression toward the terminal state. Slingshot which is one of the methods suggested for lineage reconstruction and pseudotime inference from single-cell gene expression data. In this notebook, we will illustrate an example workflow for cell lineage and pseudotime inference using Slingshot. The notebook is inspired by Slingshot's vignette and modified to demonstrate how the tool works on BioTuring's platform.