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The ability of a cell to regulate its mechanical properties is central to its function. Emerging evidence suggests that interactions between the cell nucleus and cytoskeleton influence cell mechanics through poorly understood mechanisms. Here we conduct quantitative confocal imaging to show that the loss of A-type lamins tends to increase nuclear and cellular volume while the loss of B-type lamins behaves in the opposite manner. We use fluorescence recovery after photobleaching, atomic force microscopy, optical tweezer microrheology, and traction force microscopy to demonstrate that A-type lamins engage with both F-actin and vimentin intermediate filaments (VIFs) through the linker of nucleoskeleton and cytoskeleton (LINC) complexes to modulate cortical and cytoplasmic stiffness as well as cellular contractility in mouse embryonic fibroblasts (MEFs). In contrast, we show that B-type lamins predominantly interact with VIFs through LINC complexes to regulate cytoplasmic stiffness and contractility. We then propose a physical model mediated by the lamin–LINC complex that explains these distinct mechanical phenotypes (mechanophenotypes). To verify this model, we use dominant negative constructs and RNA interference to disrupt the LINC complexes that facilitate the interaction of the nucleus with the F-actin and VIF cytoskeletons and show that the loss of these elements results in mechanophenotypes like those observed in MEFs that lack A- or B-type lamin isoforms. Finally, we demonstrate that the loss of each lamin isoform softens the cell nucleus and enhances constricted cell migration but in turn increases migration-induced DNA damage. Together, our findings uncover distinctive roles for each of the four major lamin isoforms in maintaining nucleocytoskeletal interactions and cellular mechanics.

The 'Bridging Imaging Users to Imaging Analysis' survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), BioImaging North America (BINA) and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multichoice and open‐ended questions, the survey inquired about demographics, image analysis experiences, future needs and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross‐community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents' overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user‐friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarise themselves with the fundamentals of image analysis, provide constant feedback and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for 'written tutorials' to acquire knowledge on image analysis. We also observed that the interest in having 'office hours' to get an expert opinion on their image analysis methods has increased over the years. The results also showed less‐than‐expected usage of online discussion forums in the imaging community for solving image analysis problems. Surprisingly, we also observed a decreased interest among the survey respondents in deep/machine learning despite the increasing adoption of artificial intelligence in biology. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly. [ABSTRACT FROM AUTHOR]

The cytoskeleton of eukaryotic cells is primarily composed of networks of filamentous proteins, F-actin, microtubules, and intermediate filaments. Interactions among the cytoskeletal components are important in determining cell structure and in regulating cell functions. For example, F-actin and microtubules work together to control cell shape and polarity, while the subcellular organization and transport of vimentin intermediate filament (VIF) networks depend on their interactions with microtubules. However, it is generally thought that F-actin and VIFs form two coexisting but separate networks that are independent due to observed differences in their spatial distribution and functions. In this paper, we present a closer investigation of both the structural and functional interplay between the F-actin and VIF cytoskeletal networks. We characterize the structure of VIFs and F-actin networks within the cell cortex using structured illumination microscopy and cryoelectron tomography. We find that VIFs and F-actin form an interpenetrating network (IPN) with interactions at multiple length scales, and VIFs are integral components of F-actin stress fibers. From measurements of recovery of cell contractility after transient stretching, we find that the IPN structure results in enhanced contractile forces and contributes to cell resilience. Studies of reconstituted networks and dynamic measurements in cells suggest direct and specific associations between VIFs and F-actin. From these results, we conclude that VIFs and F-actin work synergistically, both in their structure and in their function. These results profoundly alter our understanding of the contributions of the components of the cytoskeleton, particularly the interactions between intermediate filaments and F-actin. [ABSTRACT FROM AUTHOR]

In profiling assays, thousands of biological properties are measured in a single test, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles' high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise. Here, we developed a statistical framework that uses the well-established mean average precision (mAP) as a single, data-driven metric to bridge this gap. We validated the mAP framework against established metrics through simulations and real-world data applications, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we used mAP to assess both phenotypic activity for a given perturbation (or a sample) as well as consistency within groups of perturbations (or samples) across diverse high-dimensional datasets. We evaluated the framework on different profile types (image, protein, and mRNA profiles), perturbation types (CRISPR gene editing, gene overexpression, and small molecules), and profile resolutions (single-cell and bulk). Our open-source software allows this framework to be applied to identify interesting biological phenomena and promising therapeutics from large-scale profiling data., Competing Interests: Competing interests The Authors declare the following competing interests: S.S. and A.E.C. serve as scientific advisors for companies that use image-based profiling and Cell Painting (A.E.C: Recursion, SyzOnc, Quiver Bioscience, S.S.: Waypoint Bio, Dewpoint Therapeutics, Deepcell) and receive honoraria for occasional scientific visits to pharmaceutical and biotechnology companies. All other authors declare no competing interests.

We herein describe a postdoctoral training program designed to train biologists with microscopy experience in bioimage analysis. We detail the rationale behind the program, the various components of the training program, and outcomes in terms of works produced and the career effects on past participants. We analyze the results of an anonymous survey distributed to past and present participants, indicating overall high value of all 12 rated aspects of the program, but significant heterogeneity in which aspects were most important to each participant. Finally, we propose this model as a template for other programs which may want to train experts in professional skill sets, and discuss the important considerations when running such a program. We believe that such programs can have extremely positive impact for both the trainees themselves and the broader scientific community., Competing Interests: Conflicts of Interest The authors report no competing interests relevant to this paper.

Long considered active only in the germline, the PIWI/piRNA pathway is now known to play a significant role in somatic cells, especially neurons. In this study, piRNAs were profiled in the human retina and retinal pigment epithelium (RPE). Furthermore, RNA immunoprecipitation with HIWI2 (PIWIL4) in ARPE19 cells yielded 261 piRNAs, and the expression of selective piRNAs in donor eyes was assessed by qRT-PCR. Intriguingly, computational analysis revealed complete and partial seed sequence similarity between piR-hsa-26131 and the sensory organ specific miR-183/96/182 cluster. Furthermore, the expression of retina-enriched piR-hsa-26131 was positively correlated with miR-182 in HIWI2-silenced Y79 cells. In addition, the lnc-ZNF169 sequence matched with two miRNAs of the let-7 family, and piRNAs, piR-hsa-11361 and piR-hsa-11360, which could modulate the regulatory network of retinal differentiation. Interestingly, we annotated four enriched motifs among the piRNAs and found that the piRNAs containing CACAATG and CTCATCAKYG motifs were snoRNA-derived piRNAs, which are significantly associated with developmental functions. However, piRNAs consisting of ACCACTANACCAC and AKCACGYTCSC motifs were mainly tRNA-derived fragments linked to stress response and sensory perception. Additionally, co-expression network analysis revealed cell cycle control, intracellular transport and stress response as the important biological functions regulated by piRNAs in the retina. Moreover, loss of piRNAs in HIWI2 knockdown ARPE19 confirmed altered expression of targets implicated in intracellular transport, circadian clock, and retinal degeneration. Moreover, piRNAs were dysregulated under oxidative stress conditions, indicating their potential role in retinal pathology. Therefore, we postulate that piRNAs, miRNAs, and lncRNAs might have a functional interplay during retinal development and functions to regulate retinal homeostasis.

The "Bridging Imaging Users to Imaging Analysis" survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), Bioimaging North America (BINA), and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multi-choice and open-ended questions, the survey inquired about demographics, image analysis experiences, future needs, and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross-community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents' overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user-friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarize themselves with the fundamentals of image analysis, provide constant feedback, and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for 'written tutorials' to acquire knowledge on image analysis. We also observed that the interest in having 'office hours' to get an expert opinion on their image analysis methods has increased over the years. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly.