Koseki J. Kobayashi-Kirschvink

The paper introduces a microfluidic platform that traps and analyzes single cells and droplets using dielectrophoresis and Raman spectroscopy, enabling high-throughput, non-destructive cellular analysis.

The paper uses Raman spectroscopy to reveal a low-dimensional stoichiometry-conserving proteome structure across bacterial and human cells, showing its role in maintaining homeostasis and enabling adaptation.

RamanOmics integrates snRNA-seq, spatial transcriptomics, and Raman imaging to map spatial biochemical and molecular changes in aging and repair, identifying senescent cell signatures and their functional roles.

This study uses Raman spectroscopy to identify vancomycin-resistant phenotypes and their transcriptomic features in Staphylococcus aureus strains.

SCHAF generates spatially resolved single-cell transcriptomes from H&E histology images using vision transformers and adversarial learning, showing good agreement with ground-truth data.

Raman2RNA predicts single-cell RNA expression profiles from live-cell Raman microscopy images using machine learning, achieving high accuracy in tracking cell state transitions during reprogramming.

The paper links Raman spectra and transcriptomes through a shared low-dimensional subspace, enabling reconstruction of gene expression profiles from Raman data.