Deep learning pipeline for automated cell profiling from cyclic imaging.

Autor: Landeros C; Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA, 02114, USA.; Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA., Oh J; Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA, 02114, USA.; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA., Weissleder R; Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA, 02114, USA. rweissleder@mgh.harvard.edu.; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA. rweissleder@mgh.harvard.edu.; Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA. rweissleder@mgh.harvard.edu., Lee H; Center for Systems Biology, Massachusetts General Hospital, 185 Cambridge St, CPZN 5206, Boston, MA, 02114, USA. hlee@mgh.harvard.edu.; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA. hlee@mgh.harvard.edu.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Oct 09; Vol. 14 (1), pp. 23600. Date of Electronic Publication: 2024 Oct 09.
DOI: 10.1038/s41598-024-74597-w
Abstrakt: Cyclic fluorescence microscopy enables multiple targets to be detected simultaneously. This, in turn, has deepened our understanding of tissue composition, cell-to-cell interactions, and cell signaling. Unfortunately, analysis of these datasets can be time-prohibitive due to the sheer volume of data. In this paper, we present CycloNET, a computational pipeline tailored for analyzing raw fluorescent images obtained through cyclic immunofluorescence. The automated pipeline pre-processes raw image files, quickly corrects for translation errors between imaging cycles, and leverages a pre-trained neural network to segment individual cells and generate single-cell molecular profiles. We applied CycloNET to a dataset of 22 human samples from head and neck squamous cell carcinoma patients and trained a neural network to segment immune cells. CycloNET efficiently processed a large-scale dataset (17 fields of view per cycle and 13 staining cycles per specimen) in 10 min, delivering insights at the single-cell resolution and facilitating the identification of rare immune cell clusters. We expect that this rapid pipeline will serve as a powerful tool to understand complex biological systems at the cellular level, with the potential to facilitate breakthroughs in areas such as developmental biology, disease pathology, and personalized medicine.
(© 2024. The Author(s).)
Databáze: MEDLINE
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