spectroscopic-super-resolution-imaging-dna-molecules-using-intrinsic

Spectroscopic Super-resolution Imaging of DNA Molecules using Intrinsic Contrast

Super-resolution imaging has revolutionized biological research by revealing structural details at the nanoscale. Most optical super-resolution methods ...

a-multilabel-single-molecule-localization-microscopy-protocol-for

A Multilabel Single Molecule Localization Microscopy Protocol for Investigation of Chromatin in the Dense Nuclear Environment

Super-resolution microscopy has dramatically advanced our ability to interrogate biological structures beyond the diffraction limit, making it ...

Northwestern University

Chemical-genetic interaction profiling in model organisms has proven powerful in providing insights into compound mechanism of action and gene function. However, identifying chemical-genetic interactions in mammalian systems has been limited to low-throughput or computational methods. Here, we develop Quantitative and Multiplexed Analysis of Phenotype by Sequencing (QMAP-Seq), which leverages next-generation sequencing for pooled high-throughput chemical-genetic profiling. We apply QMAP-Seq to investigate how cellular stress response factors affect therapeutic response in cancer. Using minimal automation, we treat pools of 60 cell types-comprising 12 genetic perturbations in five cell lines-with 1440 compound-dose combinations, generating 86,400 chemical-genetic measurements. QMAP-Seq produces precise and accurate quantitative measures of acute drug response comparable to gold standard assays, but with increased throughput at lower cost. Moreover, QMAP-Seq reveals clinically actionable drug vulnerabilities and functional relationships involving these stress response factors, many of which are activated in cancer. Thus, QMAP-Seq provides a broadly accessible and scalable strategy for chemical-genetic profiling in mammalian cells.

Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq.

Ectomycorrhizal (EcM) fungi play important roles in forest ecosystems, and their richness and composition can change along with elevation and season changes. However, no study has estimated the relative importance of altitudinal and seasonal heterogeneity in predicting the distribution of EcM fungal communities by simultaneously considering different sample types (root versus soil). In this study, we collected root and soil samples along a > 1,500-m elevation gradient during wet and dry seasons from Baima Snow Mountain, located in "the Mountains of Southwest China," one of the 34 biodiversity hot spots, and we analyzed them using next-generation sequencing. Regardless of the sample type, similar EcM fungal richness pattern with increasing elevation (decline in the forest zone, and an increase at the alpine meadow zone) and strong community turnovers among different elevational zones and between two seasons were detected, and changes of EcM fungal community similarity on 400-m altitude gradient were equivalent to the community turnover between dry and wet seasons. Elevation and edaphic factors were shown to have the largest effects on EcM fungal community. The heterogeneity of richness and community composition was stronger among different elevational zones than across different seasons, mainly because the elevation variations in the EcM fungal community were shaped by the combined effects of different environmental factors, while seasonal changes were mainly controlled by temperature and fast-changing soil nutrients.  Altitude and season represent two important environmental gradients that shape the structure of biome, including the heterogeneity of EcM fungi. Previous studies have separately considered the influences of altitude and season on EcM fungal communities, but the relative importance of altitude and season is still unknown. The present study revealed that elevation influences the heterogeneity of EcM fungal community more than season; this may be because the variability of environmental factors is higher across different elevations than that across seasons.

Elevation Matters More than Season in Shaping the Heterogeneity of Soil and Root Associated Ectomycorrhizal Fungal Community.

This paper presents an updated full-discretization method for milling stability prediction based on cubic spline interpolation. First, the mathematical model of the time-delay milling system considering regenerative chatter is represented by a dynamic delay differential equation. Then, in a single tooth passing period, the time is divided into a finite time intervals, the state item and the time-delay item are approximated in each time interval by cubic spline interpolation and third-order Newton interpolation, respectively. Afterward, a transition matrix is constructed to represent the transfer relationship of the teeth in a period. Finally, based on Floquet theory, the milling stability lobes can be obtained. Meanwhile, in order to improve computational efficiency, an optimized method is proposed based on the traditional algorithm and the proposed method has high precision without losing high efficiency. Finally, several milling experiments are conducted to verify the accuracy of the proposed method, and the results show that the predicted results agree well with the experimental results.

A Novel Updated Full-Discretization Method for Prediction of Milling Stability.

Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets like chromatin. Chromatin exhibits a hierarchical organization with structural compartments and domains at different length scales, from nanometers to micrometers. Single molecule localization microscopy (SMLM) methods, such as STORM, are essential for studying chromatin at the supra-nucleosome level due to their ability to target epigenetic marks that determine chromatin organization. Multi-label imaging of chromatin is necessary to unpack its structural complexity. However, these efforts are challenged by the high-density nuclear environment, which can affect antibody binding affinities, diffusivity and non-specific interactions. Optimizing buffer conditions, fluorophore stability, and antibody specificity is crucial for achieving effective antibody conjugates. Here, we demonstrate a sequential immunolabeling protocol that reliably enables three-color studies within the dense nuclear environment. This protocol couples multiplexed localization datasets with a robust analysis algorithm, which utilizes localizations from one target as seed points for distance, density and multi-label joint affinity measurements to explore complex organization of all three targets. Applying this multiplexed algorithm to analyze distance and joint density reveals that heterochromatin and euchromatin are not-distinct territories, but that localization of transcription and euchromatin couple with the periphery of heterochromatic clusters. This work is a crucial step in molecular imaging of the dense nuclear environment as multi-label capacity enables for investigation of complex multi-component systems like chromatin with enhanced accuracy.

Three-color single-molecule localization microscopy in chromatin.

Human birth weight is under stabilizing selection, seeking balance between extremes of high and low, thereby reducing fetal and maternal perinatal mortality risk. Certain combinations of maternal killer immunoglobulin-like receptor () and paternally derived fetal human leuokocyte antigen-C () alleles were previously associated with higher risk of high and low birth weight in a study with limited sample size (n=1,316). Using recently developed methods to impute  and  haplotypes using single nucleotide polymorphism (SNP) genotype data, we tested associations of fetal  and maternal  genotypes with offspring birth weight in a large sample. We imputed  haplotypes using the KIR*IMP imputation software in 10,602 mother-offspring pairs of European descent from singleton pregnancies from five studies. Using mixed linear regression models to account for mothers with multiple children, we tested associations between maternal  vs  haplotypes (,  genotypes) as well as copy number of activating receptor gene  (0, 1, 2 copies of the gene) in the presence of fetal  alleles, and offspring birth weight. Associations were analyzed in each cohort before performing a meta-analysis to estimate the interaction effects between maternal  and fetal  on birth weight across the entire sample. The  haplotypes achieved imputation accuracy estimated at >95% in most of the cohorts. No interaction effects were observed between either the maternal  vs.  haplotype or the maternal  locus and fetal . When specifically trying to replicate the previously associated combination of maternal  and paternally inherited fetal , there was a negligible change in offspring birth weight for each additional  allele and  of paternal origin (7g lower birth weight per allele [95% CI: -54, 40],  = 0.78). We found little evidence of association between birth weight and maternal  haplotypes or fetal  and were unable to replicate previously reported findings. Our observations reinforce the importance of replication and the use of large sample sizes in the validation of genetic associations.

Examining the association between fetal , maternal  haplotypes and birth weight.

In single cells, chromatin packs into organized structures to perform biological functions, such as RNA transcription regulation. Characterizing such structural behaviors, including packing density and mass scaling, is critical in epigenetics research. Partial wave spectroscopic (PWS) microscopy is a label-free, live-cell, high-throughput imaging modality that utilizes optical spectroscopic statistical nanosensing. Rather than resolving the exact chromatin packing structure, PWS extracts statistical packing information from spectroscopic interference signals. In this study, we evaluate its ability to characterize multiplexed chromatin packing density and mass scaling, as well as its spatial confidence interval, using finite difference time domain (FDTD) electromagnetic simulations. We validated the simulation-based analysis algorithm by comparing experimental PWS images against coregistered super-resolution acquisitions, confirming its accuracy in capturing chromatin packing metrics. We then applied this modality to live cells treated with different epigenetic agents, mapping spatial changes in chromatin packing in a high-throughput workflow.

ArticleTotal : 2	Year
Publication title	2026
Publication title	2026

Article	Year
Quantitative and multiplexed chemical-genetic phenotyping in mammalian cells with QMAP-Seq. Nature communications\| PubMed ID: 33184288	2020
Elevation Matters More than Season in Shaping the Heterogeneity of Soil and Root Associated Ectomycorrhizal Fungal Community. Microbiology spectrum\| PubMed ID: 35019700	2022
A Novel Updated Full-Discretization Method for Prediction of Milling Stability. Micromachines\| PubMed ID: 35208285	2022
Three-color single-molecule localization microscopy in chromatin. Light, science & applications\| PubMed ID: 40091134	2025
Examining the association between fetal , maternal haplotypes and birth weight. medRxiv : the preprint server for health sciences\| PubMed ID: 40297441	2025
Multiplexed Chromatin Analysis Using Optical Spectroscopic Statistical Nanosensing. ACS photonics\| PubMed ID: 40718523	2025

Geng Wang

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