A spatially resolved timeline of the human maternal-fetal interface.

Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade the uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels. Several mechanisms have been proposed to explain how EVTs coordinate with the maternal decidua to promote a tissue microenvironment conducive to spiral artery remodelling (SAR)1-3. However, it remains a matter of debate regarding which immune and stromal cells participate in these interactions and how this evolves with respect to gestational age. Here we used a multiomics approach, combining the strengths of spatial proteomics and transcriptomics, to construct a spatiotemporal atlas of the human maternal-fetal interface in the first half of pregnancy. We used multiplexed ion beam imaging by time-of-flight and a 37-plex antibody panel to analyse around 500,000 cells and 588 arteries within intact decidua from 66 individuals between 6 and 20 weeks of gestation, integrating this dataset with co-registered transcriptomics profiles. Gestational age substantially influenced the frequency of maternal immune and stromal cells, with tolerogenic subsets expressing CD206, CD163, TIM-3, galectin-9 and IDO-1 becoming increasingly enriched and colocalized at later time points. By contrast, SAR progression preferentially correlated with EVT invasion and was transcriptionally defined by 78 gene ontology pathways exhibiting distinct monotonic and biphasic trends. Last, we developed an integrated model of SAR whereby invasion is accompanied by the upregulation of pro-angiogenic, immunoregulatory EVT programmes that promote interactions with the vascular endothelium while avoiding the activation of maternal immune cells.

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning.

A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1 million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.

Multiplexed Ion Beam Imaging: Insights into Pathobiology.

Next-generation tools for multiplexed imaging have driven a new wave of innovation in understanding how single-cell function and tissue structure are interrelated. In previous work, we developed multiplexed ion beam imaging by time of flight, a highly multiplexed platform that uses secondary ion mass spectrometry to image dozens of antibodies tagged with metal reporters. As instrument throughput has increased, the breadth and depth of imaging data have increased as well. To extract meaningful information from these data, we have developed tools for cell identification, cell classification, and spatial analysis. In this review, we discuss these tools and provide examples of their application in various contexts, including ductal carcinoma in situ, tuberculosis, and Alzheimer's disease. We hope the synergy between multiplexed imaging and automated image analysis will drive a new era in anatomic pathology and personalized medicine wherein quantitative spatial signatures are used routinely for more accurate diagnosis, prognosis, and therapeutic selection.

An engineered ligand trap inhibits leukemia inhibitory factor as pancreatic cancer treatment strategy.

Leukemia inhibitory factor (LIF), a cytokine secreted by stromal myofibroblasts and tumor cells, has recently been highlighted to promote tumor progression in pancreatic and other cancers through KRAS-driven cell signaling. We engineered a high affinity soluble human LIF receptor (LIFR) decoy that sequesters human LIF and inhibits its signaling as a therapeutic strategy. This engineered 'ligand trap', fused to an antibody Fc-domain, has ~50-fold increased affinity (~20 pM) and improved LIF inhibition compared to wild-type LIFR-Fc, potently blocks LIF-mediated effects in pancreatic cancer cells, and slows the growth of pancreatic cancer xenograft tumors. These results, and the lack of apparent toxicity observed in animal models, further highlights ligand traps as a promising therapeutic strategy for cancer treatment.

Mature B cells accelerate wound healing after acute and chronic diabetic skin lesions.

Chronic wounds affect 12-15% of patients with diabetes and are associated with a drastic decrease in their quality of life. Here, we demonstrate that purified mature naive B220+ /CD19+ /IgM+ /IgD+ B cells improve healing of acute and diabetic murine wounds after a single topical application. B cell treatment significantly accelerated acute wound closure by 2-3 days in wild-type mice and 5-6 days in obese diabetic mice. The treatment led to full closure in 43% of chronic diabetic wounds, as compared to only 5% in saline-treated controls. Applying equivalent numbers of T cells or disrupted B cells failed to reproduce these effects, indicating that live B cells mediated pro-healing responses. Topically applied B cell treatment was associated with significantly reduced scar size, increased collagen deposition and maturation, enhanced angiogenesis, and increased nerve growth into and under the healing wound. ß-III tubulin+ nerve endings in scars of wounds treated acutely with B cells showed increased relative expression of growth-associated protein 43. The improved healing associated with B cell treatment was supported by significantly increased fibroblast proliferation and decreased apoptosis in the wound bed and edges, altered kinetics of neutrophil infiltration, as well as an increase in TGF-ß and a significant reduction in MMP2 expression in wound granulation tissue. Our findings indicate that the timeline and efficacy of wound healing can be experimentally manipulated through the direct application of mature, naive B cells, which effectively modify the balance of mature immune cell populations within the wound microenvironment and accelerate the healing process.

Human fetal dendritic cells promote prenatal T-cell immune suppression through arginase-2.

During gestation the developing human fetus is exposed to a diverse range of potentially immune-stimulatory molecules including semi-allogeneic antigens from maternal cells, substances from ingested amniotic fluid, food antigens, and microbes. Yet the capacity of the fetal immune system, including antigen-presenting cells, to detect and respond to such stimuli remains unclear. In particular, dendritic cells, which are crucial for effective immunity and tolerance, remain poorly characterized in the developing fetus. Here we show that subsets of antigen-presenting cells can be identified in fetal tissues and are related to adult populations of antigen-presenting cells. Similar to adult dendritic cells, fetal dendritic cells migrate to lymph nodes and respond to toll-like receptor ligation; however, they differ markedly in their response to allogeneic antigens, strongly promoting regulatory T-cell induction and inhibiting T-cell tumour-necrosis factor-α production through arginase-2 activity. Our results reveal a previously unappreciated role of dendritic cells within the developing fetus and indicate that they mediate homeostatic immune-suppressive responses during gestation.