Suliana Manley

Pathogen survival relies on their ability to adapt to changing environments, yet how intracellular bacteria tolerate cell-autonomous immunity remains unclear. Septins assemble into cage-like structures around cytoplasmic Shigella targeted by autophagy, but their antibacterial activity was unknown. Using advanced microscopy and cell-free reconstitution, we show that septins polymerize into circumferentially oriented filaments that inhibit bacterial growth. RNA-sequencing reveals that septin binding to Shigella upregulates type III secretion system (T3SS) effectors and triggers a stress response. Underlying this, septin-bound Shigella possess hyperpolarized membranes, but without lethally damaged cell envelope. We show that exposure to septin binding enhances the impact of the cell envelope-targeting antibiotic polymyxin B. During infection, T3SS effector OspC1 prevents septin recruitment, while stress-response regulator cspC protects against septin-mediated changes to membrane potential. Hence, septin binding is intrinsically antibacterial and is sensed by Shigella as a danger signal to promote mechanisms of intracellular tolerance.

Large-scale mitochondrial DNA (mtDNA) deletions cripple oxidative phosphorylation once they exceed a critical heteroplasmy threshold, causing incurable mitochondrial pathologies. Using a genome-wide CRISPR/Cas9 screen in an engineered human cell line carrying a large-scale mtDNA deletion at high heteroplasmy, we identified mitochondrial transcription termination factor 1 (MTERF1) as a suppressor of the heteroplasmy burden. Loss of MTERF1 restored mitochondrial function and increased cellular proliferation in cells with a mtDNA deletion burden exceeding the pathogenic threshold, without altering heteroplasmy or mtDNA copy number. MTERF1 binds wild-type and deletion-bearing mitochondrial genomes indiscriminately at a site downstream of the ribosomal RNA genes and curbs transcription. Relieving this constraint broadly increased OXPHOS transcripts, thereby eliciting more respiratory output from the residual wild-type genomes. Notably, the buffering effect of MTERF1 loss extended beyond mtDNA deletions. In a counter-screen, MTERF1 loss could also restore respiratory growth in cells depleted of nuclear-encoded mitochondrial genes such as OPA1 and COX5A . Together, these findings indicate that by relieving a transcriptional constraint, MTERF1 loss compensates for reduced genome dosage, defining a strategy to enhance residual mitochondrial function in mtDNA deletion disorders and related conditions.

The distribution of mitochondrial DNA–containing nucleoids is essential for mitochondrial function and genome inheritance; however, no known mechanisms can explain nucleoid segregation or their regular positioning. In this work, we found that mitochondria frequently undergo a reversible biophysical instability termed “pearling,” transforming from a tubular into a regularly spaced beads morphology. Physiological pearling imposed a characteristic length scale and simultaneously mediated nucleoid disaggregation and established internucleoid distancing with high precision. Pearling onset was triggered by calcium influx, whereas the density of lamellar cristae invaginations modulated pearling prevalence and preserved nucleoid spacing following recovery. The dysregulation of mitochondrial calcium influx or inner membrane cristae integrity caused aberrant nucleoid clustering. Our results identify pearling as a mechanism governing nucleoid distribution and inheritance and offer insights into its regulation.

BACKGROUND: The adult mammalian heart lacks the significant regenerative potential needed to cope with the massive loss of cardiomyocytes following myocardial infarction. Ultimately, irreversible cardiac damage leads to heart failure, which is associated with a poor prognosis. Given this, reactivating dormant regenerative processes in the injured heart represents an attractive therapeutic approach. When regeneration does occur, newly formed cardiomyocytes are derived from preexisting ones. METHODS: We aimed to identify novel regulators of cardiomyocyte proliferation. In this context, the genome is transcribed for a large part into RNAs with little or no protein-coding potential. Among noncoding RNAs, long noncoding RNAs represent the most diverse class of molecules and are implicated in numerous epigenetic mechanisms, making them ideal targets for controlling cell identity and behavior. In this project, we developed a high-throughput screening assay to identify long noncoding RNAs that promote cardiomyocyte proliferation upon knockdown. Using a stringent selection pipeline, we identified Clipper , an enhancer-associated long noncoding RNA regulating the expression of its cognate protein-coding gene Lpin1 in cis. RESULTS: Clipper was found to control mitochondrial biogenesis via LPIN1 (Lipin1). Specifically, productive mitochondrial division, characterized by fission site positioning at the midzone of the mitochondrion, was stimulated by Clipper or Lpin1 silencing. The process was associated with a change in mitochondrial bioenergetics, particularly decreased oxidative metabolism, reduced production of reactive oxygen species, and dampened DNA damage, creating favorable conditions for cardiomyocyte proliferation. Clipper knockdown in vivo following myocardial infarction stimulated cardiac regeneration in the damaged myocardium, leading to the restoration of heart function. Importantly, CLIPPER is positionally and functionally conserved in humans. CONCLUSIONS: Our data identify CLIPPER as a promising therapeutic target for heart regeneration, acting through control of LPIN1-dependent mitochondrial biogenesis and cardiomyocyte proliferation.

Spatially mapping protein abundance in situ can offer important insights into molecular mechanisms and the physiological functions of protein complexes. This is typically achieved by combining super-resolution microscopy to image fluorescently-labeled protein locations with statistical estimators to retrieve abundances, where accuracy is strongly impacted by labeling efficiency. We introduce quantitative expansion microscopy (qExM) as a method to estimate endogenous protein abundance on ExM data, which offers improved antibody targeting through molecular decrowding. Using cryo-fixation, we preserved ultrastructure and enhanced labeling efficiency to improve accuracy in abundance estimations. We benchmark the effectiveness of qExM by quantifying the stoichiometry of well-characterized nuclear pore complex subunits, and find a mean percent error of 9.4%. We further apply qExM to investigate the abundance of mitochondrial respiratory chain complexes in functionally distinct organelle subpopulations, and of mitochondrial respiratory chain super-complexes in differentially activated human T-cells. qExM provides a robust methodological framework for quantifying endogenous protein abundance in expanded samples in situ .

Fluorescence microscopy offers unparalleled access to the spatial organization and dynamics of biological events in living samples, yet capturing rare processes over extended durations remains challenging due to trade-offs between exposure to excitation light and sample health. Here, we introduce hybrid-EDA, an event-driven acquisition (EDA) framework that combines the gentleness and contextual richness of phase-contrast with the functional specificity of fluorescence. We develop surveillance for events of interest in label-free microscopy using dynamics-informed neural networks that trigger smart fluorescence acquisitions upon detection. This allows us to dramatically reduce phototoxic damage while obtaining specific and functional information from fluorescence when beneficial. We demonstrate how hybrid-EDA enables improved imaging acquisitions of organelle contacts and mitochondrial divisions. We envision that hybrid-EDA will enable insights into a range of dynamic and rare biological processes, providing a powerful and general strategy for cell-friendly imaging.

In functionally polarized cells, mitochondria can form distinct subpopulations, positioned at sites of varying metabolic and energetic demands. Thus far, the potential presence of such subpopulations and implications of their intracellular trafficking in immobile and proliferative cells remains largely undescribed, despite such cells serving as key models. Here, we use substrate micropatterning to create reproducible morphologies of cultured immortalized cells, enabling us to define mitochondrial subpopulations and follow their trafficking by photoactivation. We discovered that mitochondrial material is dispersed asymmetrically throughout the cell via biased anterograde transport from the perinuclear area. Combining quantitative analysis and in silico modeling, we characterize the causes and consequences of unbalanced mitochondrial trafficking. Our findings indicate that this bias is required to distribute new material resulting from perinuclear mitochondrial biosynthesis to sustain mitochondrial mass distribution across the cell, and to maintain normal network connectivity.

Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances in computer vision, most of the currently used spot detection techniques are still based on classical signal processing and require tedious manual tuning per dataset. Here we introduce Spotiflow, a deep learning method for subpixel-accurate spot detection that formulates spot detection as a multiscale heatmap and stereographic flow regression problem. Spotiflow supports 2D and 3D images, generalizes across different imaging conditions and is more time and memory efficient than existing methods. We show the efficacy of Spotiflow by extensive quantitative experiments on diverse datasets and demonstrate that its increased accuracy leads to meaningful improvements in biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow.

Super-resolution microscopy (SRM) has undeniable potential for scientific discovery, yet still presents many challenges that hinder its widespread adoption, including technical trade-offs between resolution, speed and photodamage, as well as limitations in imaging live samples and larger, more complex biological structures. Furthermore, SRM often requires specialized expertise and complex instrumentation, which can deter biologists from fully embracing the technology. In this Perspective, a follow-up to our recent Q&A article, we aim to demystify these challenges by addressing common questions and misconceptions surrounding SRM. Experts offer practical insights into how biologists can maximize the benefits of SRM while navigating issues such as photobleaching, image artifacts and the limitations of existing techniques. We also highlight recent developments in SRM that continue to push the boundaries of resolution. Our goal is to equip researchers with the crucial knowledge they need to harness the full potential of SRM.