The human placenta is a dynamic and heterogeneous organ critical in the establishment of the fetomaternal interface and the maintenance of gestational well-being. It is also the major source of cell-free fetal nucleic acids in the maternal circulation. Placental dysfunction contributes to significant complications, such as preeclampsia, a potentially lethal hypertensive disorder during pregnancy. Previous studies have identified significant changes in the expression profiles of preeclamptic placentas using whole-tissue analysis. Moreover, studies have shown increased levels of targeted RNA transcripts, overall and placental contributions in maternal cell-free nucleic acids during pregnancy progression and gestational complications, but it remains infeasible to noninvasively delineate placental cellular dynamics and dysfunction at the cellular level using maternal cell-free nucleic acid analysis. In this study, we addressed this issue by first dissecting the cellular heterogeneity of the human placenta and defined individual cell-type-specific gene signatures by analyzing more than 24,000 nonmarker selected cells from full-term and early preeclamptic placentas using large-scale microfluidic single-cell transcriptomic technology. Our dataset identified diverse cellular subtypes in the human placenta and enabled reconstruction of the trophoblast differentiation trajectory. Through integrative analysis with maternal plasma cell-free RNA, we resolved the longitudinal cellular dynamics of hematopoietic and placental cells in pregnancy progression. Furthermore, we were able to noninvasively uncover the cellular dysfunction of extravillous trophoblasts in early preeclamptic placentas. Our work showed the potential of integrating transcriptomic information derived from single cells into the interpretation of cell-free plasma RNA, enabling the noninvasive elucidation of cellular dynamics in complex pathological conditions.
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http://dx.doi.org/10.1073/pnas.1710470114 | DOI Listing |
Cancer Metab
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Department of Dermatology, Venereology and Allergology, University Medical Center and Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, Mannheim, 68167, Germany.
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View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Biology Department, University of Massachusetts Amherst, Amherst, MA, USA.
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View Article and Find Full Text PDFFunct Integr Genomics
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Department of Cardiology, Guizhou Provincial People`s Hospital, 83 Zhongshan East Road, Guiyang City, 550002, Guizhou Province, China.
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View Article and Find Full Text PDFNat Comput Sci
January 2025
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
How complex phenotypes emerge from intricate gene expression patterns is a fundamental question in biology. Integrating high-content genotyping approaches such as single-cell RNA sequencing and advanced learning methods such as language models offers an opportunity for dissecting this complex relationship. Here we present a computational integrated genetics framework designed to analyze and interpret the high-dimensional landscape of genotypes and their associated phenotypes simultaneously.
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