Explainable machine learning-assisted exploration of chromatin dynamics reveals chromosome-specific response to serum starvation
Curation statements for this article:-
Curated by eLife
eLife Assessment
This interesting study adapts machine learning tools to analyze movements of a chromatin locus in living cells in response to serum starvation. The machine learning approach developed is useful, the experiments are well controlled, and the data are solid. The study would be greatly strengthened by testing key predictions made using perturbation experiments. This work will be of interest to those studying chromosome biology and gene expression patterns.
This article has been Reviewed by the following groups
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
- Evaluated articles (eLife)
Abstract
Abstract
Chromatin is dynamic at all length scales, influencing chromatin-based processes, such as gene expression. Even large-scale reorganization of whole chromosome territories has been reported upon specific signals, but lack of suitable methods has prevented analysis of the underlying dynamic processes. Here we have used CRISPR-Sirius for time-lapse imaging of chromatin loci dynamics during serum starvation. We show that the chromosome 1 loci move towards the nuclear envelope during the first hour of serum starvation in a chromosome-specific manner. Machine learning-assisted exploration of acquired multiparametric data combined with the Shapley values-based explanation approach allowed us to uncover the critical features that characterize chromatin dynamics during serum starvation. This analysis reveals that although serum starvation affects overall nuclear morphology and chromatin dynamics, chromosome 1 loci display a specific response that is characterized by maintenance of dynamics in constrained environment, and long “jumps” at the nuclear periphery. Interestingly, the two homologous chromosomes display differential behaviors, with the more peripheral homolog being more responsive to the signal than the internal one. Overall, the presented machine learning-assisted dataset exploration helps us navigate the multidimensional data to understand the underlying dynamic processes and can be applied to a wide variety of research questions in imaging and cell biology in general.
Article activity feed
-
-
-
eLife Assessment
This interesting study adapts machine learning tools to analyze movements of a chromatin locus in living cells in response to serum starvation. The machine learning approach developed is useful, the experiments are well controlled, and the data are solid. The study would be greatly strengthened by testing key predictions made using perturbation experiments. This work will be of interest to those studying chromosome biology and gene expression patterns.
-
Reviewer #1 (Public review):
Summary:
Redchuk et al. explore the dynamic properties of chromatin upon serum starvation using machine learning approaches. They use CRISPR-tagging to visualize a region on chromosome 1 in human cells and show that in their system, chromosome 1, but not the previously reported chromosomes 10, 13, and X, undergo a change in radial position upon serum starvation. Live cell imaging showed a position change towards the periphery after serum starvation. They then apply a machine learning algorithm for the analysis of the imaging data, which reveals changes in nuclear area during serum starvation and longer displacements of the chromosome 1 locus near the nuclear periphery. Differential behavior of homologues is also reported.
Strengths:
(1) The study of chromatin dynamics is an interesting and important area of …
Reviewer #1 (Public review):
Summary:
Redchuk et al. explore the dynamic properties of chromatin upon serum starvation using machine learning approaches. They use CRISPR-tagging to visualize a region on chromosome 1 in human cells and show that in their system, chromosome 1, but not the previously reported chromosomes 10, 13, and X, undergo a change in radial position upon serum starvation. Live cell imaging showed a position change towards the periphery after serum starvation. They then apply a machine learning algorithm for the analysis of the imaging data, which reveals changes in nuclear area during serum starvation and longer displacements of the chromosome 1 locus near the nuclear periphery. Differential behavior of homologues is also reported.
Strengths:
(1) The study of chromatin dynamics is an interesting and important area of research.
(2) The use of machine learning approaches to analyze live cell imaging data is timely.
(3) With serum starvation, the authors use a simple, well-controllable model system.
Weaknesses:
(1) This study only provides limited new insight into chromatin dynamics.
(2) It was not immediately evident what the use of machine learning approaches added to this study. It appears that the main conclusions could have been reached by conventional analysis.
(3) There are several specific technical points:
a) It was not clear what the CRISRP-Sirius probes actually labelled. The chromosome 1 sgRNA sequence is provided, but I could not find information as to which region(s) of the chromosome are actually labelled (size, location, etc.).
b) The authors visualize a relatively small region of chromosome 1 but make conclusions regarding the entire chromosome. Additional probes on the same chromosome should be used.
Related to this point, the discussion of why the authors are unable to reproduce the prior findings of relocation of chromosomes 10, 13, and X is not satisfying. It would be worth comparing the FISH-based painting of entire chromosomes, which generated the results suggesting relocation of these chromosomes, with the point-labelling method used here.
c) The study lacks controls. Since in their hands chromosomes 10, 13, and X do not change position, they should be used as a negative control in all experiments demonstrating a shift in the location of chromosome 1.
d) I did not find information about the spatial or temporal resolution of the imaging modality. This is important to assess whether the observed changes in position, relative to time, are meaningful.
e) The authors analyze surprisingly early timepoints (up to 40 minutes) of serum starvation. Would these results look different if longer serum starvation timepoints of several hours were analyzed?
f) The authors can do a better job of explaining what the biological meaning of the various parameters (DistR, TDist, etc.) they measure is.
g) I did not understand the reasoning for the authors' conclusion of differential behavior of homologues. Please explain this better, or idealy use more direct labeling methods that identify the individual homologues.
h) In many figures, statistical analysis of the data is missing, including, but not limited to, Figures 1B, C, G, Figures 4, 5, 6.
i) No information is provided throughout the manuscript as to how many cells were analyzed in each experiment. This should be indicated in every figure legend.
-
Reviewer #2 (Public review):
Summary:
The study demonstrates that CRISPR-Sirius provides a powerful approach to investigating chromosome dynamics in living cells during environmental stress. By focusing on serum starvation, the authors show that this process induces global nuclear changes, including a reduction in nuclear area and increased morphological dynamism, while at the same time driving specific reorganization of chromosome 1. Chromosome 1 relocates toward the nuclear periphery and displays distinctive patterns of motion, maintaining overall motility but punctuated by occasional long-distance displacements, particularly near the nuclear envelope. Importantly, the analysis reveals that homologous copies of chromosome 1 do not behave uniformly: peripheral loci become more mobile and responsive to starvation, whereas central …
Reviewer #2 (Public review):
Summary:
The study demonstrates that CRISPR-Sirius provides a powerful approach to investigating chromosome dynamics in living cells during environmental stress. By focusing on serum starvation, the authors show that this process induces global nuclear changes, including a reduction in nuclear area and increased morphological dynamism, while at the same time driving specific reorganization of chromosome 1. Chromosome 1 relocates toward the nuclear periphery and displays distinctive patterns of motion, maintaining overall motility but punctuated by occasional long-distance displacements, particularly near the nuclear envelope. Importantly, the analysis reveals that homologous copies of chromosome 1 do not behave uniformly: peripheral loci become more mobile and responsive to starvation, whereas central homologs remain comparatively stable, often associated with nucleolar subcompartments. By integrating live imaging with machine learning and explainable AI analysis, the study highlights the complexity of nuclear organization and provides valuable insights into how chromosome-specific and locus-specific responses to stress are orchestrated within the three-dimensional nuclear landscape.
Strengths:
The study uses live-cell imaging to investigate the dynamics of loci during starvation. Live-cell tracking and data interpretation are carried out using machine learning and AI models, which is a major strength.
Weaknesses:
The manuscript is at times difficult to follow, partly because the methodological descriptions are highly specialized, especially for non-expert biologists. In addition, the observations are not tested for a mechanistic basis. Experiments that could provide deeper insights are missing, for example, why chromosome 1 moves, why the peripheral homologue dislocates, or why a "long jump" is observed at the periphery even though the speed of the loci does not change. It is also unclear whether a displacement of 0.5 μm is functionally meaningful.
-
