ArticleProceedings of the National Academy of Sciences of the United States of America2020
A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Label-free biochemical imaging and time point analysis of neural organoids via deep learning-enhanced Raman microspectroscopy.Science advances · 2026Article
- Multimodal High-Throughput Screening Raman Spectroscopy for Label-Free Single-Cell Characterization of Recombinant Protein Production in Baculovirus Expression Vector Systems.Analytical chemistry · 2026Article
- Feasibility study on the application of Raman spectroscopy in the diagnosis of glioma.Translational cancer research · 2026Article
- RamEx: an R package for high-throughput microbial ramanome analyses with accurate quality assessment.Microbiome · 2026Article
- Ultrasmall episymbiont Nanosynbacter lyticus employs multiple ATP-generating metabolic pathways during horizontal transmission.The ISME journal · 2026Article
- Advanced Imaging for Live-Cell Spatiotemporal Monitoring: Technologies and Applications.Research (Washington, D.C.) · 2026Review
- Single-cell Raman profiling of B cell differentiation and leukemic transformation with transcriptome inference.Frontiers in immunology · 2026Article
- Fluorescence Guided Raman Spectroscopy enables the training of robust support vector machines for the detection of tumour marker proteins.Scientific reports · 2025Article
- Noninvasive andThe Analyst · 2025Article
- Current Trends in In Vitro Diagnostics Using Surface-Enhanced Raman Scattering in Translational Biomedical Research.Biosensors · 2025Review
- Raman analysis of lipids in cells: Current applications and future prospects.Journal of pharmaceutical analysis · 2025Review
- Label-Free Detection of Biochemical Changes during Cortical Organoid Maturation via Raman Spectroscopy and Machine Learning.Analytical chemistry · 2025Article
- Adaptive Raman spectral unmixing method based on Voigt peak compensation for quantitative analysis of cellular biochemical components.Biomedical optics express · 2025Article
- High-content stimulated Raman pathology imaging and transcriptomics reveal leukemia subtype-specific lipid metabolic heterogeneity.Frontiers in immunology · 2025Article
- Unveiling the Molecular Secrets: A Comprehensive Review of Raman Spectroscopy in Biological Research.ACS omega · 2024Review
- Label-Free Raman Spectroscopy for Assessing Purity and Maturation of hiPSC-Derived Cardiac Tissue.Analytical chemistry · 2024Article
- Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification.PNAS nexus · 2024Article
- Neural Network-Based Filter Design for Compressive Raman Classification of Cells.Journal of chemical information and modeling · 2024Article
- Real-Time, Non-Invasive Monitoring of Neuronal Differentiation Using Intein-Enabled Fluorescence Signal Translocation in Genetically Encoded Stem Cell-Based Biosensors.Advanced functional materials · 2024Article
- Label-Free Assessment of Neuronal Activity Using Raman Micro-Spectroscopy.Molecules (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural diseases. Nevertheless, clinical applications of stem cells have been hindered partly owing to a lack of standardized techniques to characterize cell molecular profiles noninvasively and comprehensively. Here, we demonstrate that a label-free and noninvasive single-cell Raman microspectroscopy (SCRM) platform was able to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). By analyzing the intrinsic biochemical profiles of single cells at a large scale (8,774 Raman spectra in total), iPSCs and iPSC-derived neural cells can be distinguished by their intrinsic phenotypic Raman spectra. We identified a Raman biomarker from glycogen to distinguish iPSCs from their neural derivatives, and the result was verified by the conventional glycogen detection assays. Further analysis with a machine learning classification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in different developmental stages with 97.5% accuracy. The present study demonstrates the capability of the SCRM-based platform to monitor cell development using high content screening with a noninvasive and label-free approach. This platform as well as our identified biomarker could be extensible to other cell types and can potentially have a high impact on neural stem cell therapy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.