Feature selection for preserving biological trajectories in single-cell data.

Single-cell technologies can readily measure the expression of thousands of molecular features from individual cells undergoing dynamic biological processes, such as cellular differentiation, immune response, and disease progression. While examining cells along a computationally ordered pseudotime offers the potential to study how subtle changes in gene or protein expression impact cell fate decision-making, identifying characteristic features that drive continuous biological processes remains difficult to detect from unenriched and noisy single-cell data. Given that all profiled sources of feature variation contribute to the cell-to-cell distances that define an inferred cellular trajectory, including confounding sources of biological variation (e.g. cell cycle or metabolic state) or noisy and irrelevant features (e.g. measurements with low signal-to-noise ratio) can mask the underlying trajectory of study and hinder inference. Here, we present DELVE (dynamic selection of locally covarying features), an unsupervised feature selection method for identifying a representative subset of dynamically-expressed molecular features that recapitulates cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effect of unwanted sources of variation confounding inference, and instead models cell states from dynamic feature modules that constitute core regulatory complexes. Using simulations, single-cell RNA sequencing data, and iterative immunofluorescence imaging data in the context of the cell cycle and cellular differentiation, we demonstrate that DELVE selects features that more accurately characterize cell populations and improve the recovery of cell type transitions. This feature selection framework provides an alternative approach for improving trajectory inference and uncovering co-variation amongst features along a biological trajectory. DELVE is implemented as an open-source python package and is publicly available at: https://github.com/jranek/delve.

Mll1 pioneers histone H3K4me3 deposition and promotes formation of CD8 + T stem cell memory.

CD8 + T cells with stem cell-like properties (T SCM ) sustain adaptive immunity to intracellular pathogens and tumors. However, the developmental origins and chromatin regulatory factors (CRFs) that establish their differentiation are unclear. Using an RNA interference screen of all CRFs we discovered the histone methylase Mll1 was required during T cell receptor (TCR) stimulation for development of a T SCM precursor state and mature memory (T MEM ) cells, but not short-lived or transitory effector cell-like states, in response to viral infections and tumors. Mll1 was essential for widespread de novo deposition of histone H3 lysine 4 trimethylation (H3K4me3) upon TCR stimulation, which accounted for 70% of all activation-induced sites in mature T MEM cells. Mll1 promoted both H3K4me3 deposition and reduced TCR-induced Pol II pausing at genes whose single-cell transcriptional dynamics explained trajectories into nascent T SCM precursor states during viral infection. Our results suggest Mll1-dependent control of Pol II elongation and H3K4me3 establishes and maintains differentiation of CD8 + T SCM cell states.

The Transcription Factor Runx3 Establishes Chromatin Accessibility of cis-Regulatory Landscapes that Drive Memory Cytotoxic T Lymphocyte Formation.

T cell receptor (TCR) stimulation of naive CD8+ T cells initiates reprogramming of cis-regulatory landscapes that specify effector and memory cytotoxic T lymphocyte (CTL) differentiation. We mapped regions of hyper-accessible chromatin in naive cells during TCR stimulation and discovered that the transcription factor (TF) Runx3 promoted accessibility to memory CTL-specific cis-regulatory regions before the first cell division and was essential for memory CTL differentiation. Runx3 was specifically required for accessibility to regions highly enriched with IRF, bZIP and Prdm1-like TF motifs, upregulation of TFs Irf4 and Blimp1, and activation of fundamental CTL attributes in early effector and memory precursor cells. Runx3 ensured that nascent CTLs differentiated into memory CTLs by preventing high expression of the TF T-bet, slowing effector cell proliferation, and repressing terminal CTL differentiation. Runx3 overexpression enhanced memory CTL differentiation during iterative infections. Thus, Runx3 governs chromatin accessibility during TCR stimulation and enforces the memory CTL developmental program.

Residual stress measurements via neutron diffraction of additive manufactured stainless steel 17-4 PH.

Neutron diffraction was employed to measure internal residual stresses at various locations along stainless steel (SS) 17-4 PH specimens additively manufactured via laser-powder bed fusion (L-PBF). Of these specimens, two were rods (diameter=8 mm, length=80 mm) built vertically upward and one a parallelepiped (8×80×9 mm3) built with its longest edge parallel to ground. One rod and the parallelepiped were left in their as-built condition, while the other rod was heat treated. Data presented provide insight into the microstructural characteristics of typical L-PBF SS 17-4 PH specimens and their dependence on build orientation and post-processing procedures such as heat treatment. Data have been deposited in the Data in Brief Dataverse repository (doi:10.7910/DVN/T41S3V).