Nature Methods

Within AI4Life, we ran three Open Calls and three Public Challenges (2023-2025), supporting 22 bioimage analysis projects from 151 applications and engaging 225 challenge participants, with the aim of applying FAIR deep learning in the life sciences. Our experience offers a view of the current state of bioimage analysis, the landscape of available tools, as well as the existing gaps between method developers, tool producers and potential users. It highlights that even after careful selection for AI-ready projects, most still require substantial effort to apply deep learning, and that the field still relies heavily on established, well-rounded methods to solve common problems. We come to the conclusion that for scientific AI in biology, the rate-limiting step is not methods and models but data, annotations, and shared infrastructure underneath them.

Motion artifacts are almost unavoidable in functional time-lapse and structural volumetric multiphoton microscopy. They arise from respiration, heartbeat, locomotion, awake behavior, instrument heating, mechanical vibration, and slow drift, while the recorded signal is often photon-limited, blurred by scattering, and biologically time varying. Consequently, motion correction is frequently an essential prerequisite for quantitative bioimage analysis rather than a merely cosmetic preprocessing operation. Edge- and landmark-centric registration strategies are often poorly matched to these data because useful structures may be sparse, diffuse, out-of-focus, or changing in fluorescence intensity. We present ZenReg, an open-source Python platform that formulates common 2D+t, 3D, and 3D+t microscopy registration tasks as a modular family of geometry-preserving alignment problems. ZenReg combines Fourier phase correlation, intensity-based StackReg-style alignment, NoRMCorre-style piecewise translation fields, projection-based rotation estimates, dense SimpleITK-based six-degree-of-freedom volume registration, and sparse point-based rigid-volume registration within one canonical microscopy stack model. The platform uses OMIO to normalize heterogeneous microscope files and to preserve metadata, while optional disk-backed Zarr arrays support chunked, memory-efficient processing of image stacks that exceed available memory or reside on remote storage. ZenReg writes registered images together with shift tables, correlation metrics, summary plots, and machine-readable settings. In synthetic benchmarks with known ground truth, ZenReg recovered global 2D and 3D translations with subpixel accuracy across moderate noise and drift regimes, while high-noise and large-drift tests separated the backend behavior: FFT-based methods failed abruptly once image information or shared support became insufficient, StackReg degraded more gradually under severe noise, and piecewise NoRMCorre improved spatially varying local-motion correction where a single global transform was inadequate. Parallel execution reduced runtime for large time series, and ZenReg provided practical full-volume rigid correction for dense and puncta-rich 3D+t stacks. By coupling a modular, extensible backend architecture to transparent sidecar outputs, ZenReg makes motion correction easier to extend, inspect, share, reproduce, and reuse as part of scientific image analysis.

We developed EASI-PASS, a reliable, high-throughput method for estimating the molecular identity of functionally characterized cells by merging live imaging with subsequent fixed-tissue imaging using conventional microscopes. Our method matches the shapes and locations of thousands of densely imaged cells between large (>1 mm2) functional images and a thick, expanded, and cleared EASI-FISH tissue volume to assess gene expression. This approach is more efficient than alignment to thin sections and recovers the molecular identity of ~78% of cells. In acute brain slice imaging from the mouse parabrachial nucleus during optogenetic stimulation of long-range spinal inputs, we observed fine-scale specificity in the molecular identity of spinorecipient neurons. In the awake mouse visual cortex, we observed distinct arousal modulation and spatial falloff in correlations within and across interneuron classes. Thus, EASI-PASS provides reliable and efficient alignment of cellular activity with molecular identity.

Understanding distributed biological systems, particularly neural circuits, requires simultaneous cellular-resolution imaging across millimeter-scale fields of view (FOV). Existing miniature microscopes remain fundamentally constrained by trade-offs among FOV, spatial resolution, and optical complexity, limiting their ability to bridge cellular microscopy with cortex-scale imaging. Here we introduce distributed computational optics, a framework that distributes image formation across coordinated optical modules and computationally integrates their measurements into a unified image. We realize this framework in Bio-CM2, a computational miniature mesoscope that partitions the imaging field across four optical modules while converging their measurements onto a common image sensor. This architecture overcomes the aberration-scaling limitations of conventional miniature optics while avoiding the hardware complexity of multi-camera systems and the contrast degradation associated with optical multiplexing. Bio-CM2 achieves a 7.5 x 10 mm2 FOV while enabling cellular-resolution in vivo imaging at video rates. We demonstrate its utility through two complementary imaging modalities in head-fixed mice: cortex-wide functional vascular imaging, enabling simultaneous quantification of pial arteriole vasomotion and mesoscale hemodynamic functional connectivity, and cellular-resolution calcium imaging, resolving the activity of over 3,000 neurons together with mesoscale neuronal functional connectivity. We further demonstrate the versatility of the platform through cellular-resolution imaging of entire coronal mouse brain sections, population-scale imaging of freely behaving Caenorhabditis elegans, and odor-evoked calcium imaging of the main olfactory bulb in head-fixed mice, highlighting its broad applicability across diverse biological systems and imaging modalities. By overcoming the conventional trade-off between FOV and spatial resolution in a compact miniature platform, Bio-CM2 establishes distributed computational optics as a scalable framework for multiscale biological imaging.

While Single Molecule Localization Microscopy (SMLM) aims to generate precise coordinates of molecular targets in cells, the resulting point clouds are inherently blurred by additive noise sources across the experimental, imaging, and processing workflow. This blurring often limits SMLMs ability to accurately quantify complex assembled structures required to address biological issues, despite reported localization precision down to a couple of nanometers. Here, we present SMLMFlow, a machine learning framework for improving structural resolution in SMLM datasets that combines a graph neural network and a hierarchical transformer with flow matching. We show that SMLMFlow improves structural resolution and downstream quantification across different structures, including filaments and protein nano-clusters, and generalizes to new unseen photophysics models.

Biological image analysis is full of discrete decisions: whether an object is present, which of several overlapping detections is real, whether two detections match across time, and whether a cell divides. Standard pipelines resolve them locally with non-max suppression, thresholding, or greedy linking, committing before all image and temporal evidence is in. We recast such problems as discrete inverse rendering: candidate renderings are generated then jointly selected to reconstruct the movie subject to temporal and biological constraints, solved to certified optimality with a modern integer-programming solver. The same formulation covers suppression of overlapping detections, selection of a structure as a path, and event-structured tracking with birth, death, and division. Applied to C. elegans splines, sperm flagella, and dividing cells, the method matches specialised state-of-the-art pipelines across three imaging modalities on a single objective, with the largest gains where per-frame segmentation is unreliable (on a low-signal fluorescence movie of Huh7 hepatoma cells, detection F1 doubles from 0.31 to 0.58). HighlightsO_LIInteger-programming framework for suppression, path, and lineage selection C_LIO_LIOne objective, one solver: worm splines, sperm flagella, and dividing cells C_LIO_LIReconstruction-based scoring matches specialised pipelines across three modalities C_LIO_LICertified-optimal solutions in seconds to minutes on standard benchmark movies C_LI In BriefZdyb and Kirkegaard recast several biological image-analysis problems--non-max suppression, structural path extraction, and event-structured cell tracking--as one discrete inverse-rendering problem solved by an integer-programming solver, matching specialised trained pipelines across three distinct imaging modalities within a single objective.

Structured illumination microscopy (SIM) enables rapid, long-term super-resolution (SR) imaging of live-cell dynamics. However, although current state-of-the-art (SOTA) SIM reconstruction methods achieve high-fidelity structural SR, they consistently lack reliable intensity quantification, restricting their use in quantitative biology. Here, we develop qHiFi-SIM, a physics-driven self-supervised learning framework for quantitative high-fidelity SR-SIM imaging. By leveraging the wide-field image as a physical intensity reference, our approach enables self-supervised training without reliance on SR data with ground-truth intensity. qHiFi-SIM achieves high structural fidelity (structural similarity, SSIM > 0.95) with a twofold resolution enhancement, while maintaining excellent intensity linearity (coefficient of determination, R{superscript 2} > 0.99). It also exhibits strong transferability across diverse SIM setups and typical samples, and is compatible with SOTA SIM algorithms, enabling direct quantitative correction of their intensity deviations without retraining. We demonstrate the unique advantages of qHiFi-SIM for live-cell quantitative visualization of mitochondrial structure, intensity, and membrane potential dynamics, as well as for high-fidelity SIM-FRET (Forster resonance energy transfer) functional imaging. We anticipate that qHiFi-SIM will serve as a practical tool for SR structural visualization and quantitative functional imaging in live cells.

Calcium transients encode cellular and neuronal activity across timescales ranging from milliseconds to hours, yet linking these transient signals to downstream molecular states remains a major challenge. We recently introduced Caprola, a calcium-dependent protein labeling tool that converts calcium transients into permanent fluorescent marks for later analysis. In this way, Caprola enables tracking of neuronal activities in animal models as well as retrospective identification of labeled cells for isolation and transcriptomic analysis. However, the relatively slow labeling kinetics of Caprola required high concentrations of fluorophore probe and relatively long labeling times, which limits its sensitivity and applicability, in particular in vivo. To address this limitation, we generated Caprola variants with up to 29-fold faster labeling rates than their predecessor. We demonstrate that our new Caprola variants record calcium transients in cells and in zebrafish larval brains under conditions where previous Caprola variants did not show labeling. We further expand the applicability of Caprola to activity-dependent marking of postsynaptic compartments, opening new avenues for coupling functional activity histories with downstream molecular and transcriptomic analyses.

Structural characterization of macromolecular assemblies within intact cells remains one of the central challenges in cell biology. While cryo-electron tomography provides unparalleled structural information, its applicability is limited by sample thickness, imaging throughput, and accessibility. Fluorescence microscopy offers molecular specificity and compatibility with intact biological specimens but has so far lacked the spatial resolution required to visualize cellular ultrastructure. Here we introduce Mega-expansion microscopy (Mega-ExM), a fluorescence imaging approach that enables structural visualization of whole cells using conventional confocal microscopes. Mega-ExM combines iterative hydrogel expansion with whole-proteome NHS-dye labeling and post-expansion immunostaining to achieve tunable expansion factors of up to [~]1,500-fold while preserving ultrastructure. At expansion factors of 40-260x, Mega-ExM resolves centrioles, mitochondrial cristae, protein-dense domains within mitochondrial cristae consistent with respiratory-chain supercomplexes, the synaptonemal complex, and nuclear pore complexes (NPCs) with high fidelity. Particle averaging of [~]200x expanded NPCs yields reconstructions with a structural resolution of [~]35 [A], approaching what cryo-electron tomography has achieved for selected protein assemblies. By combining molecular specificity, large imaging volumes, and nanoscale structural resolution on conventional fluorescence microscopes, Mega-ExM establishes a broadly accessible platform for in situ structural biology.

Single-cell spatial transcriptomics can connect molecular cell states with tissue morphology, but this promise depends on accurate registration to histopathology. In serial sections, however, tissue borders often differ because of sectioning artifacts, staining variability, and field-of-view acquisition, limiting conventional area-based registration. We developed AnchorR, an expert-guided workflow for coarse-grained alignment of hematoxylin and eosin (H&E) images with CosMx Spatial Molecular Imaging data. Bioinformaticians first define and color-code cell types in Seurat, and pathologists then identify corresponding internal landmarks using QuPath overlays. AnchorR combines these paired landmarks to estimate affine transformations, quantify residual error, and support visual quality control and anchor refinement. Using six oral pre-cancerous tissue sections, we identified 60 cross-modal landmarks. Fitting each section independently reduced mean landmark error from 121.5 {micro}m with a single whole-slide transformation to 14.6 {micro}m. Cross-validation further showed that increasing the number of anchors improved robustness, with nine-anchor fits achieving approximately 20 {micro}m error, or about one cell diameter. AnchorR is designed to complement automated computer-vision methods by providing reliable tissue-level alignment when border mismatch makes global registration difficult. By creating a shared workspace for pathologists and bioinformaticians, it operationalizes an expert-in-the-loop approach and makes feature-based multimodal registration accessible without specialized computer-vision expertise or high-performance computing.

Laboratory discovery increasingly depends on computational workflows that connect experimental data to analysis, interpretation and follow-up hypotheses. Yet these workflows remain constrained by labor-intensive use of specialized software, visual inspection through graphical user interfaces, and integration of knowledge across multiple sources. Here, we present Orion, a computer-using AI agent for biomedical image analysis and interpretation that moves towards lab automation by automating this computational layer of laboratory work. Orion combines large language models with terminal execution, GUI control and adaptive multi-step reasoning in a shared computing environment. It can inspect visual data, operate standard scientific software, mine web resources and conduct end-to-end analysis and interpretation workflows without requiring bespoke software integrations. Across benchmarks, Orion achieved over 90% accuracy on biomedical database and literature retrieval tasks, learned to use the popular tools CellProfiler and QuPath for quantitative analysis of cellular and tissue images, respectively, and facilitated autonomous discovery in experimental imaging data. In 100 hours of autonomous exploration of a large-scale perturbation imaging dataset, Orion generated 52 research reports, of which human scientist review prioritized 22 plausible mechanistic hypotheses. These results show that computer-using AI agents can substantially expand the reach of laboratory automation, providing a scalable and auditable route from experimental imaging data to quantitative analysis, reports and biologically grounded hypotheses. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC="FIGDIR/small/732095v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@476af5org.highwire.dtl.DTLVardef@bf1ac4org.highwire.dtl.DTLVardef@765c18org.highwire.dtl.DTLVardef@983419_HPS_FORMAT_FIGEXP M_FIG Overview of Orion Orion operates within a digital lab environment, using both graphical user interfaces and terminals just like a human scientist. This dual approach allows Orion to interact seamlessly with scientific software while also viewing figures and web databases to capture their nuanced visual information. C_FIG

The Human Reference Atlas (HRA) enables multiscale data exploration and visualization. We present "HRA: Powers of Ten," a virtual reality (VR) application for integrating, harmonizing, and visualizing data within the HRA Organ Gallery. It enables immersive navigation from a whole-body view of 81 organs to datasets across 5 organs, 5 assay types, and 4 spatial scales using a Multiscale Elevator System. The application, data, and code are available open-source.

Three-dimensional fluorescence microscopy often exhibits anisotropic resolution because axial information is poorly sampled and more blurred than lateral information, which complicates quantitative interpretation of fine 3D structures. Although optical remedies and computational restoration have been explored, many approaches require demanding system calibration or rely on accurate PSF models and assumptions that are difficult to satisfy across all samples and modalities. Here we present DeepIso, a self-supervised isotropy restoration framework that couples supervised pretraining with an internal-learning inference stage to estimate degradation directly from the measured volume. Without explicit PSF specification or enforced lateral-axial structural equivalence, DeepIso recovers axial frequency content and improves the continuity of elongated structures while retaining fine features, with superior performance over existing computational approaches in terms of both visual inspection and quantitative metrics. The method is validated on synthetic benchmarks and experimental datasets, demonstrating isotropy enhancement across confocal, light-sheet, and 3D structured illumination microscopy, thereby supporting downstream volumetric analysis including segmentation and tracking.

Volume electron microscopy is essential for understanding cells, tissues, and neural circuits in their native 3D context, but many biological specimens are too large to image exhaustively at nanometer resolution. Researchers therefore must choose between broad anatomical context and ultrastructural detail. We introduce random-access electron microscopy (RAEM), a framework for studying fixed tissue repeatedly across scales rather than imaging it once at a single resolution. RAEM first builds a lower resolution 3D survey of the specimen, then uses accumulated human or AI-derived knowledge of that volume to guide the microscope back to selected physical sites for high resolution imaging. By linking reconstructed 3D coordinates to precise electron-beam positions on the original sections, RAEM enables targeted imaging of membranes, vesicles, and other nanoscale structures within specimens that would be impractical to image exhaustively. We demonstrate RAEM with vesicle-resolved imaging of synaptic boutons in human cortex, targeted imaging of more than one million human cortical mitochondria, hierarchical imaging of a nematode nervous system, and retrospective targeting of a previously published petabyte-scale human cortical volume. RAEM turns serial-section EM into a query-driven, multi-resolution approach for scalable biomedical discovery.

Label-free optical redox imaging utilizes endogenous NAD(P)H and FAD autofluorescence to evaluate metabolism in living specimens. The conventional optical redox ratio collapses these two channels into a single value; however, it does not indicate whether a pixel has sufficient photon support or the cellular context necessary for quantitative aggregation. To address this limitation, we introduce FPhaS, a fixed-calibration phase- autofluorescence framework that integrates quantitative phase imaging (QPI) with simultaneous label-free autofluorescence multi-harmonic microscopy (SLAM), using fluorescence lifetime imaging (FLIM) solely for validation. Because QPI and SLAM are acquired with the same objective, a unified non-biological calibration aligns phase-derived structural data with the autofluorescence frame, yielding a residual error of 0.39 pixels. This calibration is maintained across all biological specimens. This shared geometric reference enables local evaluation of structural and metabolic information, rather than comparing approximately aligned images. FPhaS decomposes the data into cell presence, ratio credibility, and confidence-supported pooling. We validated FPhaS on A549 cells under high and low-photon conditions; the framework is designed to generalize to other cell and tissue types. Confidence-weighted intensity redox estimates were compared with lifetime-derived measurements within mask-locked cellular regions. Concordance improved exclusively when both the denominator photon support and an independent structural criterion were satisfied. The same reference layer generated cell-level descriptors of metabolic content, metabolic-structural organization, and measurement reliability, while also constraining the CombinedWLS reconstruction under diminished fluorescence acquisition. FPhaS redefines label-free metabolic imaging from producing comprehensive ratio maps to identifying regions where optical evidence substantiates quantitative inference.

Background. Three-dimensional reconstruction from serial spatial-transcriptomics (ST) sections requires registering adjacent slices, but physical sectioning introduces tears -- discontinuous, non-isometric deformations. Leading methods rely on priors that tears strain: PASTE/PASTE2 use Fused Gromov-Wasserstein optimal transport (OT), which assumes near-isometric preservation of within-slice distances, while STalign and CODA use diffeomorphic (LDDMM) mapping, which cannot change tissue topology. Learned-deformation ST methods are emerging (STaCker, INST-Align), but OT/diffeomorphic behaviour under tearing has not been systematically characterised. Methods. On the spatialLIBD human DLPFC Visium dataset (Maynard et al., 2021; 3 donors), we build a controlled benchmark -- known smooth warps, single-block rigid tears (expression unchanged), and an identity self-control -- at severities of 0-8 spot pitches, scored against an approximate array-position ground truth (~8 px residual). We evaluate three unsupervised incumbents -- PASTE2 (OT, over five warp seeds), STalign (diffeomorphic LDDMM), and GPSA (Gaussian-process warp) -- add a magnitude-matched smooth control, and test a minimal graph model, Sutura (per-slice graph encoder -> cross-attention correspondence -> per-spot displacement; spatial coupling is local kNN message passing only, no explicit smoothness penalty). Sutura is trained supervised on each tissue's ground truth; all baselines are unsupervised. Generalisation is assessed by leave-one-donor-out across all three donors. Results. OT registration is robust to smooth warps but degrades reproducibly under tearing: nearest-correspondence (argmax) error 722 +/- 5 -> 855 +/- 27 px and layer accuracy 64.9% -> 60.5% (mean +/- 95% CI, 5 seeds). The effect is not merely displacement magnitude: at a matched mean displacement (~2000 px), a smooth warp costs 769 px / 60.2% accuracy whereas a tear costs 863 px / 57.5% -- an extra ~100 px and ~3 points attributable to the discontinuity. STalign (LDDMM) and GPSA (GP warp) both collapse at severe tears (866 px and 931 px respectively), confirming tear-collapse is field-wide across three independent method families. Trained and evaluated on the same donor, Sutura fits torn-tissue correspondence to a median 99 -> 106 px (5-seed), but under leave-one-donor-out is 1236 +/- 2 -> 1584 +/- 52 px -- approximately 1.8-3.6x worse than PASTE2 on every unseen donor. A contrastive correspondence loss halves the gap on two of three donors (to 816 -> 949 and 749 -> 826 px, approximately 1.1-1.2x PASTE2 at worst-case tear) but is modest on the third and never surpasses PASTE2. Conclusion. Tearing is a real, magnitude-controlled failure mode of all three incumbent method classes. A learned model fits it in-sample but donor-invariant generalisation remains open. The contrastive fix roughly halves the held-out gap on two of three donors and nears PASTE2 at worst-case tear, but does not surpass it: donor-invariance is improved, not solved. The durable contribution is the benchmark, the characterisation across three method families, and an honest negative with a diagnosed mechanism.

Multiplex immunofluorescent imaging enables deep spatial profiling of protein expression in tissues but is often limited by reliance on proprietary reagents, dedicated hardware, and closed analysis ecosystems. Here we present CYCLOPS (Cyclic Open Platform for Spatial Proteomics), an end-to-end, open-source workflow for cyclic multiplex imaging and single-cell phenotyping using standard microscopy infrastructure. CYCLOPS integrates an Arduino-based automated fluidics system, an open-chamber stage insert, and antibody-oligonucleotide conjugation based entirely on published chemistries and off-the-shelf components. We demonstrate robust and reproducible antibody conjugation, high-quality multiplexed staining, and stable imaging across >10 cycles with minimal drift (<1 {micro}m) and consistent fluorescence retention with low signal carry-over. The system supports efficient buffer exchange and consistent performance across multiple markers and imaging rounds. Using confocal microscopy, the workflow is compatible with three-dimensional imaging, enabling multiplexed analysis of volumetric tissue structures. To enable quantitative analysis, we establish an open-source image processing and analysis pipeline for single-cell feature extraction and phenotypic classification, avoiding reliance on proprietary software or black-box workflows. This framework integrates image registration, segmentation, and supervised classification to generate biologically interpretable single-cell data. Together, CYCLOPS provides a flexible and accessible platform for cyclic multiplex imaging, lowering barriers to adoption and enabling broader use of spatial proteomics across diverse research settings. This accessible framework democratizes high-plex imaging by enabling any laboratory with a standard confocal microscope to perform iterative multiplexing without reliance on proprietary reagents or hardware.

Advances in fluorescence microscopy have dramatically expanded the range of biological questions that can be addressed, enabling quantitative observations of molecular interactions and cellular dynamics with unprecedented spatial and temporal resolution. However, the growing complexity of imaging data has outpaced our ability to analyze them. Despite numerous computational methods exist, they often rely on specialized software environments, heterogeneous data formats, and technical expertise, limiting adoption and widening the gap between data acquisition and quantitative biological interpretation. Here we introduce BioBrain, a multi-agent framework that translates natural-language analytical goals into executable and reproducible microscopy analysis pipelines. Instead of generating analysis code, BioBrain assembles validated analytical methods and can expands its analytical capabilities by integrating existing laboratory scripts into a unified conversational framework. Every selected method and inferred parameter is transparently reported, ensuring traceable and reproducible analyses. On two-channel total internal reflection fluorescence and three-dimensional lattice light-sheet benchmarks, BioBrain exactly reproduces expert-derived results when parameters are specified and degrades predictably and traceably when they are not, while frontier language models generated large, model-dependent quantitative errors despite completing without warning. BioBrain offers a practical path for closing the widening gap between data acquisition and biological discovery, enabling experimental scientists to communicate with computational analysis in the language of biology rather than the language of software.

Understanding how cells establish spatial organization within tissues is a fundamental question in life sciences. While modern three-dimensional fluorescence microscopy captures large-volume tissue architecture, extracting quantitative cellular insights from complex volumetric datasets remains a major barrier. Here, we introduce FOCUS-3D, a robust, broadly generalizable volumetric cell segmentation framework built on a large, diverse manually annotated cell resource and advanced AI designs. Integrating volumetric representation learning, multi-scale feature extraction, and query-based mask prediction, FOCUS-3D achieves state-of-the-art performance across diverse species, tissues, fluorescent reporters and imaging modalities. During zebrafish (Danio rerio) development, FOCUS-3D uncovers three successive phases of notochord morphogenesis. We disentangle early motility-driven rearrangements from later cell shape remodeling and tissue repacking, and further link these morphological states to spatial and developmental transcriptional programs across independent datasets.

Quantitative cell colocalization in fluorescence microscopy often depends on ad hoc combinations of image loading, segmentation, region selection, manual inspection, and spreadsheet post-processing. Such workflows are difficult to transfer across projects and often obscure how intermediate results were produced. We present CellColoc, an open-source Python workflow pipeline for segmentation-based cell colocalization, single-channel segmentation, and cell feature extraction in 2D and 3D microscopy images. CellColoc provides a modular workflow layer that integrates existing segmentation backends, including Cellpose and threshold-based methods, into reusable, script-driven analyses. The package supports channel-wise backend selection, interactive or reusable regions of interest, optional third-channel occupancy and cell-positivity analysis, z-cropping and z-projection, cached post hoc refinement of Cellpose thresholds, and reanalysis after manual mask editing. Analyses are executed from concise user scripts while reusable functionality is kept in a core package. Intermediate artifacts such as ROI masks, per-channel label masks, positive-cell masks, and structured result tables are written to a standardized results directory, promoting transparent inspection, reproducibility, and FAIR-aligned reuse. Public example datasets, a synthetic benchmark, and archived software releases accompany the package. By separating reusable analysis logic from project-specific configuration, CellColoc offers an extensible foundation for community-driven microscopy workflows that need transparent per-cell overlap classification, morphology readouts, and reusable batch analysis.