T helper 2 cells control monocyte to tissue-resident macrophage differentiation during nematode infection of the pleural cavity.

The recent revolution in tissue-resident macrophage biology has resulted largely from murine studies performed in C57BL/6 mice. Here, using both C57BL/6 and BALB/c mice, we analyze immune cells in the pleural cavity. Unlike C57BL/6 mice, naive tissue-resident large-cavity macrophages (LCMs) of BALB/c mice failed to fully implement the tissue-residency program. Following infection with a pleural-dwelling nematode, these pre-existing differences were accentuated with LCM expansion occurring in C57BL/6, but not in BALB/c mice. While infection drove monocyte recruitment in both strains, only in C57BL/6 mice were monocytes able to efficiently integrate into the resident pool. Monocyte-to-macrophage conversion required both T cells and interleukin-4 receptor alpha (IL-4Rα) signaling. The transition to tissue residency altered macrophage function, and GATA6+ tissue-resident macrophages were required for host resistance to nematode infection. Therefore, during tissue nematode infection, T helper 2 (Th2) cells control the differentiation pathway of resident macrophages, which determines infection outcome.

In vivo labeling reveals continuous trafficking of TCF-1+ T cells between tumor and lymphoid tissue.

Improving the efficacy of immune checkpoint therapies will require a better understanding of how immune cells are recruited and sustained in tumors. Here, we used the photoconversion of the tumor immune cell compartment to identify newly entering lymphocytes, determine how they change over time, and investigate their egress from the tumor. Combining single-cell transcriptomics and flow cytometry, we found that while a diverse mix of CD8 T cell subsets enter the tumor, all CD8 T cells retained within this environment for more than 72 h developed an exhausted phenotype, revealing the rapid establishment of this program. Rather than forming tumor-resident populations, non-effector subsets, which express TCF-1 and include memory and stem-like cells, were continuously recruited into the tumor, but this recruitment was balanced by concurrent egress to the tumor-draining lymph node. Thus, the TCF-1+ CD8 T cell niche in tumors is highly dynamic, with the circulation of cells between the tumor and peripheral lymphoid tissue to bridge systemic and intratumoral responses.

Kidney organoids recapitulate human basement membrane assembly in health and disease.

Basement membranes (BMs) are complex macromolecular networks underlying all continuous layers of cells. Essential components include collagen IV and laminins, which are affected by human genetic variants leading to a range of debilitating conditions including kidney, muscle, and cerebrovascular phenotypes. We investigated the dynamics of BM assembly in human pluripotent stem cell-derived kidney organoids. We resolved their global BM composition and discovered a conserved temporal sequence in BM assembly that paralleled mammalian fetal kidneys. We identified the emergence of key BM isoforms, which were altered by a pathogenic variant in COL4A5. Integrating organoid, fetal, and adult kidney proteomes, we found dynamic regulation of BM composition through development to adulthood, and with single-cell transcriptomic analysis we mapped the cellular origins of BM components. Overall, we define the complex and dynamic nature of kidney organoid BM assembly and provide a platform for understanding its wider relevance in human development and disease.

A living biobank of ovarian cancer ex vivo models reveals profound mitotic heterogeneity.

High-grade serous ovarian carcinoma is characterised by TP53 mutation and extensive chromosome instability (CIN). Because our understanding of CIN mechanisms is based largely on analysing established cell lines, we developed a workflow for generating ex vivo cultures from patient biopsies to provide models that support interrogation of CIN mechanisms in cells not extensively cultured in vitro. Here, we describe a "living biobank" of ovarian cancer models with extensive replicative capacity, derived from both ascites and solid biopsies. Fifteen models are characterised by p53 profiling, exome sequencing and transcriptomics, and karyotyped using single-cell whole-genome sequencing. Time-lapse microscopy reveals catastrophic and highly heterogeneous mitoses, suggesting that analysis of established cell lines probably underestimates mitotic dysfunction in advanced human cancers. Drug profiling reveals cisplatin sensitivities consistent with patient responses, demonstrating that this workflow has potential to generate personalized avatars with advantages over current pre-clinical models and the potential to guide clinical decision making.

ZIC3 Controls the Transition from Naive to Primed Pluripotency.

Embryonic stem cells (ESCs) must transition through a series of intermediate cell states before becoming terminally differentiated. Here, we investigated the early events in this transition by determining the changes in the open chromatin landscape as naive mouse ESCs transition to epiblast-like cells (EpiLCs). Motif enrichment analysis of the newly opening regions coupled with expression analysis identified ZIC3 as a potential regulator of this cell fate transition. Chromatin binding and genome-wide transcriptional profiling following Zic3 depletion confirmed ZIC3 as an important regulatory transcription factor, and among its targets are genes encoding a number of transcription factors. Among these is GRHL2, which acts through enhancer switching to maintain the expression of a subset of genes from the ESC state. Our data therefore place ZIC3 upstream of a set of pro-differentiation transcriptional regulators and provide an important advance in our understanding of the regulatory factors governing the early steps in ESC differentiation.

An improved constraint filtering technique for inferring hidden states and parameters of a biological model.

MOTIVATION: In systems biology, kinetic models represent the biological system using a set of ordinary differential equations (ODEs). The correct values of the parameters within these ODEs are critical for a reliable study of the dynamic behaviour of such systems. Typically, it is only possible to experimentally measure a fraction of these parameter values. The rest must be indirectly determined from measurements of other quantities. In this article, we propose a novel statistical inference technique to computationally estimate these unknown parameter values. By characterizing the ODEs with non-linear state-space equations, this inference technique models the unknown parameters as hidden states, which can then be estimated from noisy measurement data. RESULTS: Here we extended the square-root unscented Kalman filter SR-UKF proposed by Merwe and Wan to include constraints with the state estimation process. We developed the constrained square-root unscented Kalman filter (CSUKF) to estimate parameters of non-linear state-space models. This probabilistic inference technique was successfully used to estimate parameters of a glycolysis model in yeast and a gene regulatory network. We showed that our method is numerically stable and can reliably estimate parameters within a biologically meaningful parameter space from noisy observations. When compared with the two common non-linear extensions of Kalman filter in addition to four widely used global optimization algorithms, CSUKF is shown to be both accurate and computationally efficient. With CSUKF, statistical analysis is straightforward, as it directly provides the uncertainty on the estimation result. AVAILABILITY AND IMPLEMENTATION: Matlab code available upon request from the author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.