Research
'Live-cell omics' with Raman2RNA
Single-cell RNA-seq and other omics assays have provided unprecedented insight into cellular properties, regulation, dynamics, and function. However, because these assays are inherently destructive, they do not allow us to track temporal changes in living cells. To overcome this limitation, we developed Raman2RNA (R2R), an experimental and computational framework that infers single-cell expression profiles in live cells using Raman microscopy and machine learning. Raman2RNA can be used to unravel principles that govern cellular dynamics, such as the emergence of cellular heterogeneity and the evolution of cell states in embryo genesis or emergence of tumors from healthy tissue.
Koseki J. Kobayashi-Kirschvink †, …, Peter So, Tommaso Biancalani†, Jian Shu†, Aviv Regev†, “Raman2RNA: Live-cell label-free prediction of single-cell RNA profiles by Raman microscopy,” Nature Biotechnology, 2024. link †corresponding authors
Koseki J. Kobayashi-Kirschvink†, Hidenori Nakaoka, Arisa Oda, Ken-ichiro F. Kamei, Kazuki Nosho, Hiroko Fukushima, Yu Kanesaki, Shunsuke Yajima, Haruhiko Masaki, Kunihiro Ohta, Yuichi Wakamoto†: “Linear Regression Links Transcriptomic Data and Cellular Raman Spectra,” Cell Systems, 2018. link †corresponding authors. Featured in Science
High-throughput Raman microscopy
Raman microscopy is a powerful tool that reports on the vibration energy levels of molecules non-destructively, but traditional systems are often limited by their speed and sensitivity, severely limiting its broad usage. I have developed high-throughput Raman microscopy systems that use novel optical designs and machine learning algorithms to improve the speed and sensitivity. These systems can be used to study a wide range of biological samples, including live cells and tissues.
Koseki J. Kobayashi-Kirschvink, Jeon Woong Kang, Peter T.C. So, “High-speed Raman microscopy by total internal reflection,” manuscript under preparation, patent filed
Koseki J. Kobayashi-Kirschvink, Alex Matlock, Peter So, Jeonwoong Kang, “High-throughput Raman spectroscopy by horizontally shifted collection fibers,” Analytical Chemistry, 2024. link cover highlight
Spatial multiomic landscape of the human placenta
I also was involved in a project that mapped the spatial multiomic landscape of the human placenta at molecular resolution. This work involved the use of state-of-the-art spatial multiomics methods to study the structure and function of the placenta at a cellular level. We developed a new method to infere gene expression dynamics with transcriptomics and chromatin accessibility information, predicting the near-term future of cell fates from genomic profiles alone. The results of this study have important implications for our understanding of placental biology and its role in human health and disease.
Johain R. Ounadjela*, Ke Zhang*, Koseki J. Kobayashi-Kirschvink*, …, Fei Chen, Sandra Heider, Jian Shu, “Spatial multiomic landscape of the human placenta at molecular resolution,” Nature Medicine, 2024. link *co-first authors