Meads, M. | Search bioRxiv

bioRxiv2024

Single-cell transcriptomics reveal differences between chorionic and basal plate cytotrophoblasts and trophoblast stem cells

Cytotrophoblast (CTB) of the early gestation human placenta are bipotent progenitor epithelial cells, which can differentiate into invasive extravillous trophoblast (EVT) and multinucleated syncytiotrophoblast (STB). Trophoblast stem cells (TSC), derived from early first trimester placentae, have also been shown to be bipotential. In this study, we set out to probe the transcriptional diversity of first trimester CTB and compare TSC to various subgroups of CTB. We performed single-cell RNA sequencing on six normal placentae, four from early (6-8 weeks) and two from late (12-14 weeks) first trimester, of which two of the early first trimester cases were separated into basal (maternal) and chorionic (fetal) fractions prior to sequencing. We also sequenced three TSC lines, derived from 6-8 week placentae, to evaluate similarities and differences between primary CTB and TSC. CTB clusters displayed notable distinctions based on gestational age, with early first trimester placentae showing enrichment for specific CTB subtypes, further influenced by origin from the basal or chorionic plate. Differential expression analysis of CTB from basal versus chorionic plate highlighted pathways associated with proliferation, unfolded protein response, and oxidative phosphorylation. We identified trophoblast states representing initial progenitor CTB, precursor STB, precursor and mature EVT, and multiple CTB subtypes. CTB progenitors were enriched in early first trimester placentae, with basal plate cells biased toward EVT, and chorionic plate cells toward STB, precursors. Clustering and trajectory inference analysis indicated that TSC were most like EVT precursor cells, with only a small percentage of TSC on the pre-STB differentiation trajectory. This was confirmed by flow cytometric analysis of 6 different TSC lines, which showed uniform expression of proximal column markers ITGA2 and ITGA5. Additionally, we found that ITGA5+ CTB could be plated in 2D, forming only EVT upon spontaneous differentiation, but failed to form self-renewing organoids; conversely, ITGA5-CTB could not be plated in 2D, but readily formed organoids. Our findings suggest that distinct CTB states exist in different regions of the placenta as early as six weeks gestation and that current TSC lines most closely resemble ITGA5+ CTB, biased toward the EVT lineage.

developmental biology↗

bioRxiv2024

TLCellClassifier: Machine Learning Based Cell Classification for Bright-Field Time-Lapse Images

Immunotherapies have shown promising results in treating patients with hematological malignancies like multiple myeloma, which is an incurable but treatable bone marrow-resident plasma cell cancer. Choosing the most efficacious treatment for a patient remains a challenge in such cancers. However, pre-clinical assays involving patient-derived tumor cells co-cultured in an ex vivo reconstruction of immune-tumor micro-environment have gained considerable notoriety over the past decade. Such assays can characterize a patients response to several therapeutic agents including immunotherapies in a high-throughput manner, where bright-field images of tumor (target) cells interacting with effector cells (T cells, Natural Killer (NK) cells, and macrophages) are captured once every 30 minutes for upto six days. Cell detection, tracking, and classification of thousands of cells of two or more types in each frame is bound to test the limits of some of the most advanced computer vision tools developed to date and requires a specialized approach. We propose TLCellClassifier (time-lapse cell classifier) for live cell detection, cell tracking, and cell type classification, with enhanced accuracy and efficiency obtained by integrating convolutional neural networks (CNN), metric learning, and long short-term memory (LSTM) networks, respectively. State-of-the-art computer vision software like KTH-SE and YOLOv8 are compared with TLCellClassifier, which shows improved accuracy in detection (CNN) and tracking (metric learning). A two-stage LSTM-based cell type classification method is implemented to distinguish between multiple myeloma (tumor/target) cells and macrophages/monocytes (immune/effector cells). Validation of cell type classification was done both using synthetic datasets and ex vivo experiments involving patient-derived tumor/immune cells. Availability and implementationhttps://github.com/QibingJiang/cell classification ml

cancer biology↗