Posts by Collection


Divide and conquer: A perspective on biochips for single-cell and rare-molecule analysis by next-generation sequencing

Published in APL Bioengineering, 2019

Recent advances in biochip technologies that connect next-generation sequencing (NGS) to real-world problems have facilitated breakthroughs in science and medicine. Because biochip technologies are themselves used in sequencing technologies, the main strengths of biochips lie in their scalability and throughput. Through the advantages of biochips, NGS has facilitated groundbreaking scientific discoveries and technical breakthroughs in medicine. However, all current NGS platforms require nucleic acids to be prepared in a certain range of concentrations, making it difficult to analyze biological systems of interest. In particular, many of the most interesting questions in biology and medicine, including single-cell and rare-molecule analysis, require strategic preparation of biological samples in order to be answered. Answering these questions is important because each cell is different and exists in a complex biological system. Therefore, biochip platforms for single-cell or rare-molecule analyses by NGS, which allow convenient preparation of nucleic acids from biological systems, have been developed. Utilizing the advantages of miniaturizing reaction volumes of biological samples, biochip technologies have been applied to diverse fields, from single-cell analysis to liquid biopsy. From this perspective, here, we first review current state-of-the-art biochip technologies, divided into two broad categories: microfluidic- and micromanipulation-based methods. Then, we provide insights into how future biochip systems will aid some of the most important biological and medical applications that require NGS. Based on current and future biochip technologies, we envision that NGS will come ever closer to solving more real-world scientific and medical problems.

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OPENchip: an on-chip in situ molecular profiling platform for gene expression analysis and oncogenic mutation detection in single circulating tumour cells

Published in Lab on a chip, 2020

Liquid biopsy holds promise towards practical implementation of personalized theranostics of cancer. In particular, circulating tumour cells (CTCs) can provide clinically actionable information that can be directly linked to prognosis or therapy decisions. In this study, gene expression patterns and genetic mutations in single CTCs are simultaneously analysed by strategically combining microfluidic technology and in situ mo- lecular profiling technique. Towards this, the development and demonstration of the OPENchip (On-chip Post-processing ENabling chip) platform for single CTC analysis by epithelial CTC enrichment and subse- quent in situ molecular profiling is reported. For in situ molecular profiling, padlock probes that identify specific desired targets to examine biomarkers of clinical relevance in cancer diagnostics were designed and used to create libraries of rolling circle amplification products. We characterize the OPENchip in terms of its capture efficiency and capture purity, and validate the probe design using different cell lines. By inte- grating the obtained results, molecular analyses of CTCs from metastatic breast cancer (HER2 (ERBB2) gene expression and PIK3CA mutations) and metastatic pancreatic cancer (KRAS gene mutations) patients were demonstrated without any off-chip processes. The results substantiate the potential implementation of early molecular detection of cancer through sequencing-free liquid biopsy

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Spatial epitranscriptomics reveals A-to-I editome specific to cancer stem cell microniches

Published in Nature Communications, 2022

Epitranscriptomic features, such as single-base RNA editing, are sources of transcript diversity in cancer, but little is understood in terms of their spatial context in the tumour microenvironment. Here, we introduce spatial-histopathological examination-linked epitran- scriptomics converged to transcriptomics with sequencing (Select-seq), which isolates regions of interest from immunofluorescence-stained tissue and obtains transcriptomic and epitranscriptomic data. With Select-seq, we analyse the cancer stem cell-like microniches in relation to the tumour microenvironment of triple-negative breast cancer patients. We identify alternative splice variants, perform complementarity-determining region analysis of infiltrating T cells and B cells, and assess adenosine-to-inosine base editing in tumour tissue sections. Especially, in triple-negative breast cancer microniches, adenosine-to-inosine edi- tome specific to different microniche groups is identified.

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Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning

Published in Nature Biomedical Engineering, 2022

Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do not typically consider histopathological features from the tumour microenvironment. Here, we show that a graph deep neural network that considers such contextual features in gigapixel WSIs in a semi-supervised manner can provide interpretable prognostic biomarkers. We designed a neural-network model that leverages attention techniques to learn features of the heterogenous tumour microenvironment from memory-efficient representations of aggregates of highly correlated image patches. We trained the model with WSIs of kidney, breast, lung and uterine cancers, and validated it by predicting the prognosis of 4,967 patients with these four different types of cancer. We also show that the model provides interpretable contextual features of clear cell renal cell carcinoma that allowed for the risk-based retrospective stratification of 1,333 patients. Deep graph neural networks that derive contextual histopathological features from WSIs may aid diagnostic and prognostic tasks

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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