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

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

In situ microenvironment remodeling using a dual-responsive system: photodegradable hydrogels and gene activation by visible light

A 3D microenvironment with dynamic cell-biomaterial interactions was developed using a dual-responsive system for in situ microenvironment remodeling and control of cellular function. A visible-light-responsive polymer was utilized to prepare a hydrogel with photodegradation properties, enabling in situ microenvironment remodeling. Additionally, a vascular endothelial growth factor (VEGF) gene activation unit that was responsive to the same wavelength of light was incorporated to support the potential application of the system in regenerative medicine. Following light exposure, the mechanical properties of the photodegradable hydrogel gradually deteriorated, and product analysis confirmed the degradation of the hydrogel, and thereby, 3D microenvironment remodeling. In situ microenvironment remodeling influenced stem cell proliferation and enlargement within the hydrogel. Furthermore, stem cells engineered to express light-activated VEGF and incorporated into the dual-responsive system were applied to wound healing and an ischemic hindlimb model, proving their potential application in regenerative medicine.11Nsciescopu

In situ microenvironment remodeling using a dual-responsive system: photodegradable hydrogels and gene activation by visible light

A 3D microenvironment with dynamic cell-biomaterial interactions was developed using a dual-responsive system for in situ microenvironment remodeling and control of cellular function. A visible-light-responsive polymer was utilized to prepare a hydrogel with photodegradation properties, enabling in situ microenvironment remodeling. Additionally, a vascular endothelial growth factor (VEGF) gene activation unit that was responsive to the same wavelength of light was incorporated to support the potential application of the system in regenerative medicine. Following light exposure, the mechanical properties of the photodegradable hydrogel gradually deteriorated, and product analysis confirmed the degradation of the hydrogel, and thereby, 3D microenvironment remodeling. In situ microenvironment remodeling influenced stem cell proliferation and enlargement within the hydrogel. Furthermore, stem cells engineered to express light-activated VEGF and incorporated into the dual-responsive system were applied to wound healing and an ischemic hindlimb model, proving their potential application in regenerative medicine.N

Examples of sample alignment in the TCGA BRCA data set.

<p>(A) A similarity score distribution of a correctly labeled profile. The red star indicates the similarity score between self-matched profile pairs (gene expression and methylation data profiles are labeled as pertaining to the same sample). (B) Similarity scores of self-matched pairs (red stars) between gene expression and methylation profiles for two samples are lower than the similarity scores of cross-matched pairs (blue stars).</p

MODMatcher: Multi-Omics Data Matcher for Integrative Genomic Analysis

<div><p>Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets.</p></div

Sample similarity measurement based on cis methylation-mRNA pairs.

<p>After cis methylation-mRNA pairs are identified, the methylation and gene expression levels were rank-transformed. In this figure, there are M samples and <i>i</i> cis pairs. Then Pearson correlation is calculated and used as sample similarity, , between one methylation profile and all gene expression profiles. If both methylation and gene expression profiles are from the same individual, self-self correlation coefficient is expected to be significantly higher than correlation coefficients with other samples.</p

Gender prediction based on expression of the Y-chromosome specific gene <i>RPS4Y1</i>.

<p>The log2 transformed values of <i>RPS4Y1</i> expression level are clearly separated between male and female samples both in CTRL and patients with COPD (>10 in male samples and <10 in female samples). There were no gender mismatched samples in the CTRL and 5 mismatched samples (2 in females and 3 in males) in the COPD set (error rate of 1.5%).</p

Sample alignment with MODMatcher.

<p>Initial labels of samples are used to determine cis pairs, which are then used to calculate similarity scores. Based on the similarity scores determined with three data types, the molecular data are matched with each other (1) by gender, (2) by cis-eSNPs, (3) by cis-mSNPs, (4) by cis mRNA-methylation pairs, and (5) by all trio mapping. Then, updated sample pairs are used to calculate new cis pairs for another round of alignment. Rounds of alignment are repeated until there are no further changes.</p