Optical High Content Nanoscopy of Epigenetic Marks Decodes Phenotypic Divergence in Stem Cells

While distinct stem cell phenotypes follow global changes in chromatin marks, single-cell chromatin technologies are unable to resolve or predict stem cell fates. We propose the first such use of optical high content nanoscopy of histone epigenetic marks (epi-marks) in stem cells to classify emergent cell states. By combining nanoscopy with epi-mark textural image informatics, we developed a novel approach, termed EDICTS (Epi-mark Descriptor Imaging of Cell Transitional States), to discern chromatin organizational changes, demarcate lineage gradations across a range of stem cell types and robustly track lineage restriction kinetics. We demonstrate the utility of EDICTS by predicting the lineage progression of stem cells cultured on biomaterial substrates with graded nanotopographies and mechanical stiffness, thus parsing the role of specific biophysical cues as sensitive epigenetic drivers. We also demonstrate the unique power of EDICTS to resolve cellular states based on epi-marks that cannot be detected via mass spectrometry based methods for quantifying the abundance of histone posttranslational modifications. Overall, EDICTS represents a powerful new methodology to predict single cell lineage decisions by integrating high content super-resolution nanoscopy and imaging informatics of the nuclear organization of epi-marks.National Institutes of Health (U.S.) (Grant GM110174

Made to measure: An introduction to quantifying microscopy data in the life sciences

Images are at the core of most modern biological experiments and are used as a major source of quantitative information. Numerous algorithms are available to process images and make them more amenable to be measured. Yet the nature of the quantitative output that is useful for a given biological experiment is uniquely dependent upon the question being investigated. Here, we discuss the 3 main types of information that can be extracted from microscopy data: intensity, morphology, and object counts or categorical labels. For each, we describe where they come from, how they can be measured, and what may affect the relevance of these measurements in downstream data analysis. Acknowledging that what makes a measurement 'good' is ultimately down to the biological question being investigated, this review aims at providing readers with a toolkit to challenge how they quantify their own data and be critical of conclusions drawn from quantitative bioimage analysis experiments

動物行動を理解するためのバイオインフォマティクス技術の開発

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 津田 宏治, 東京大学教授 森下 真一, 東京大学准教授 伊藤 啓, 岡山大学准教授 竹内 秀明, 東京大学准教授 岩崎 渉University of Tokyo(東京大学

A new era in bioimage informatics.

<p>Bioimage informatics arose from efforts to automate pathology and cytology tasks (<a href="http://bioinformatics.oxfordjournals.org/content/30/10/1353.long#ref-4">Eaves, 1967</a>). With few exceptions, much of the software developed during these early days, whether in academic or commercial institutions, was proprietary. The primary paradigm was production of hand-tuned engineered systems that could reproduce human performance, and visualization was emphasized for interpreting results or providing assistance to clinicians (<a href="http://bioinformatics.oxfordjournals.org/content/30/10/1353.long#ref-1">Bartels and Wied, 1977;</a> <a href="http://bioinformatics.oxfordjournals.org/content/30/10/1353.long#ref-7">Kaman, et al., 1984;</a> <a href="http://bioinformatics.oxfordjournals.org/content/30/10/1353.long#ref-13">van Driel-Kulker and Ploem, 1982</a>). The computational resources available at the time were frequently limiting. Essentially, no successful commercial systems came from these efforts for many years, until the US Food and Drug Administration’s approval of automated Pap smear analysis in the mid 1990s (<a href="http://bioinformatics.oxfordjournals.org/content/30/10/1353.long#ref-10">Patten <em>et al.</em>, 1996</a>).</p