Cytely: Automated Image Analysis for Cell-Based Microscopy What is Cytely? Cytely is a web-based software platform designed for automated image analysis of microscopic cell samples. It automates the process of identifying, quantifying, and classifying cells within complex biological samples, with particular strength in nuclei-based analysis and multi-parameter phenotyping. Core Capabilities: Automated Cell/Nuclei Identification: Automatically detects and segments individual cells or nuclei within images, even in dense populations. Quantitative Measurement: For each identified cell/nucleus, Cytely measures various parameters including: - Area: Size of the cell/nucleus - Circularity: Shape roundness - Mean DAPI Intensity: Average brightness of DAPI stain (DNA/nuclei marker) - Major and Minor Axis Length: Cell/nucleus dimensions - Channel-specific intensities: Mean, median, and max intensities across multiple fluorescence channels Data Visualization & Gating: Presents quantitative data in interactive scatter plots. Users draw "gates" (regions) on these plots to select specific cell populations based on measured characteristics, enabling filtering of debris, identification of specific cell types, and focus on cells undergoing particular processes. Real-time Image-Data Linkage: When a population is selected in scatter plots, corresponding cells are immediately highlighted in the original microscopy image and shown as cropped individual images, allowing visual verification of analyzed cell types. Multi-channel Analysis: Handles multiple fluorescent channels (e.g., DAPI, FITC, TRITC, Cy5) for multi-parameter analysis. Applications and Use Cases: 1. Cell Counting and Population Analysis - Rapidly and accurately determine total cell counts (e.g., 5,500+ cells in seconds) - Identify specific subpopulations within heterogeneous samples 2. Cell Cycle Analysis - Identify dividing cells using DAPI staining alone by analyzing nuclear characteristics (area, circularity, mean DAPI intensity, major vs. minor axis length) - Differentiate elongated nuclei typical of dividing cells (metaphase, anaphase) from interphase cells 3. Phagocytosis Assays Cytely provides specialized capabilities for phagocytosis analysis: Sample Setup: Phagocytic cells (e.g., HeLa cells, neutrophils) stained with DAPI (nuclei) and phalloidin or membrane markers, with fluorescently-labeled prey particles (e.g., FITC-labeled beads or bacteria). Key Workflow: - Edge exclusion and single cell filtering to ensure clean populations - Identification of prey-positive cells based on prey channel intensity - Quantification of prey per cell (distinguishing "few prey" vs. "many prey" populations) - Spatial distribution analysis to identify environmental effects Critical Metric - Image Multiplicity of Prey (iMOP): Cytely directly calculates iMOP (ratio of total prey to total cells) from acquired images, providing an objective, internal reference that eliminates reliance on estimated MOP values. Quantitative Outputs: - Total cell count and total prey count - Percentage of cells with prey association - Total and mean associated prey area - Associated prey signal intensity (MFI) - Phagocytosis score (composite metric) - Spatial distribution analysis showing prey consumption patterns Key Advantage: Unlike flow cytometry, Cytely retains spatial context, enabling discovery of spatial effects (e.g., uneven prey distribution due to settling) that influence phagocytic efficiency. 4. Toxin Uptake and Intracellular Localization - Determine uptake rates of fluorescently-labeled substances (e.g., Cholera toxin B subunit) - Observe intracellular localization patterns - Filter out aggregates and artifacts using multi-parameter gating (e.g., Max vs. Median intensity to separate aggregates from cellular uptake) - Visual confirmation of expected trafficking patterns (e.g., retrograde transport to Golgi) 5. Sample Purity and Quality Control - Identify and characterize contaminants in purified cell preparations - Distinguish neutrophils from contaminating red blood cells and platelets based on DAPI staining, size, and morphology - Use multi-parameter analysis (DAPI intensity, area, circularity, axis lengths, membrane/cytosolic dye intensities) to classify mixed populations - Troubleshoot unexpected flow cytometry results through visual inspection 6. Transfection Efficiency and Co-expression Analysis - Quantify single-positive and double-positive cells in multi-transfection experiments - Assess co-expression of different fluorescently-tagged plasmids (e.g., Integrin-FP and Actin-FP fusions) - Visual confirmation of co-localization in double-positive cells - Guide optimization of transfection protocols based on quantitative efficiency data 7. Phenotypic Screening and Morphological Analysis - Screen for changes in cell phenotype in response to treatments or conditions - Quantify morphological and intensity features across populations - Identify rare cell phenotypes or outliers within large datasets Key Scientific Framework: Data-Driven Microscopy (DDM) Cytely implements a Data-Driven Microscopy approach that integrates real-time image analysis with microscope control, as described in "Data-driven microscopy allows for automated context-specific acquisition of high-fidelity image data" (André et al.). Two-Phase Framework: Phase 1: Data-Independent Acquisition (DIA) - Population Overview - Acquires comprehensive, population-wide overview at low magnification (e.g., 10x) - Performs real-time image analysis on incoming images - Extracts quantitative data for every detected object (size, shape, fluorescence intensity, spatial coordinates) - Creates a "population fingerprint" stored in a database - Provides critical context for every cell in the sample, eliminating field-of-view selection bias Phase 2: Data-Dependent Acquisition (DDA) - Targeted High-Fidelity Imaging - Uses comprehensive DIA data to define specific subpopulations of interest through gating - Targets selected cells/regions for high-resolution imaging (e.g., switching from 10x to 60x) - Can employ different imaging techniques (TIRF, SIM) or initiate time-lapse imaging - Ensures rare or relevant events are captured with necessary detail DDM Advantages: - Reduces human bias through automated, quantitative cell selection - Increases reproducibility with standardized, data-driven workflows - Provides population context for single-cell data - Enables high-fidelity imaging of relevant phenotypes - Automates targeting of rare events - Curates population-wide estimates through high-fidelity verification Who is Cytely Perfect For? Cytely is designed for research and operational teams in biotech, CROs, and pharmaceutical companies who need to transform microscopy image analysis from a bottleneck into a scalable, reliable process. Ideal Use Profile: Workflow Characteristics: - You use consistent imaging methods and assay formats, even if exploring different biological questions or experimental conditions - There is a named owner of the workflow (assay lead, platform scientist, operations manager, service delivery lead) - You face operational pressure around throughput, turnaround time, analyst capacity, quality variance, or rework - You are ready to replace your current analysis process, not just trial a tool alongside existing workflows - Population-level quantification is a recurring bottleneck in your operation Note: Cytely excels at exploratory research when the core assay structure remains consistent (same cell types, staining approaches, imaging modality). It's ideal for labs running similar assays repeatedly while exploring different treatments, conditions, or biological questions. Technical Requirements: Sample Type: - Cultured cells (not tissue sections, histology, or pathology samples) - Cells with identifiable boundaries (separable, not densely fused tissue) - Standard cell culture formats (2D monolayers, treated cells, co-cultures) Imaging Setup: - Fluorescence microscopy with 2D endpoint imaging (single time point) - Multi-channel fluorescence capability (DAPI, FITC, TRITC, Cy5, etc.) - Z-stack acquisition is supported when processed to 2D (e.g., maximum intensity projections, deconvolution-enhanced images) - common in confocal workflows - Consistent imaging parameters within experiments (same channels, settings across image sets) Data Format: - Images in 2D format (or z-stacks processed to 2D) - Uncompressed files with intact metadata - Standard microscopy file formats with accessible channel information - Manageable per-file structure (multi-FOV containers may require pre-processing or clean export) Cell Masking Requirements (at least one must be true): - Clear boundary signal available (e.g., membrane stain, cytoplasmic marker) - Sufficient space between cells for segmentation - Nuclear marker present (e.g., DAPI, Hoechst) for nucleus-based cell identification Analysis Needs: - Single-cell analysis and population quantification - Fluorescence and/or morphological readouts - Multi-parameter phenotyping (combining size, shape, intensity, spatial location) - Subcellular feature quantification is well-supported for punctate structures (vesicles, foci, puncta, particles, organelles) - not filamentous or branching morphologies - For multiple subcellular object types: best suited when aggregated statistics per cell are sufficient (total count, mean intensity, etc.) rather than per-object-type separation - Co-localization analysis supported when objects can be masked across relevant channels Strong Fit Indicators: - Need to quantify subcellular objects (puncta, foci, vesicles, particles, organelles) with per-cell statistics - Require spatial analysis and context (e.g., identifying spatial gradients, locating specific cells in the sample) - Need to link single-cell imaging back to population context - Want to automate gating and classification that's currently done manually - Require visual verification of algorithmic classifications - Need to identify and image rare cell populations or events What Cytely is Not Designed For: Sample Types: - Histology, pathology, or tissue sections - Whole-slide imaging of tissue specimens - Dense, fused tissue where individual cell boundaries are not identifiable Analysis Types: - 3D volumetric analysis (note: z-stack acquisition processed to 2D projections is fine) - Time-series analysis, cell tracking, or dynamic event monitoring - Live-cell imaging with temporal analysis - Filamentous or branching morphology quantification (e.g., neurite tracing, vessel networks) Use Cases: - Projects where imaging methods change drastically from experiment to experiment (e.g., constantly switching between different cell types, staining protocols, or microscopy modalities) - Workflows with fully validated, locked-in pipelines where any process changes are prohibited - True one-off custom segmentation challenges with no intention of applying the workflow again Getting Started: Cytely transforms microscopy image analysis through: - Automated, unbiased cell identification and quantification - Multi-parameter gating for precise population selection - Real-time visual feedback linking data to images - Spatial analysis capabilities unavailable in flow cytometry - Scalable workflows that eliminate manual analysis bottlenecks If your team runs repeated cell-based microscopy assays and population-level quantification is slowing you down, Cytely can help you achieve faster, more reliable, and more scalable analysis. CASE STUDY LIBRARY Here are some examples of how Cytely has been deployed. #1 — Antibody Screening | Industry Company: Swedish Life Sciences Company Pain point: Flow cytometry lacks subcellular detail; manual microscopy too slow for screening Result: Faster go/no-go decisions, de-risked candidate selection, fewer dead-end follow-ups Quote: "Cytely will give us the reproducibility we need to make confident go/no-go calls on our antibody candidates." Link: https://www.cytely.io/case-studies/antibody-adcp-screening #2 — Nanowire Analysis | Industry Company: Swedish Nanowire Biotech Startup Pain point: Slow experiment cycles, manual image quantification at scale Result: 40% faster experiment cycles, 67% throughput increase (45→75/year), 7,800+ images processed Link: https://www.cytely.io/case-studies/biotech-nanowire-analysis #3 — Herpesvirus Research (Blazej Cegielski, PhD researcher) | Academia Lab: Evilevitch Lab, Lund University Pain point: 3 years stuck troubleshooting transfection/infection efficiency by manually checking 5-20 cells Result: First Cytely session revealed transfection was triggering an antiviral response — a flaw invisible at small sample sizes. Thousands of cells analyzed instantly. Video: https://www.youtube.com/watch?v=NeQA7erg8bo Link: https://www.cytely.io/case-studies/evilevitch-lab-virology-bottleneck #4 — Herpesvirus Research (Prof. Alex Evilevitch, PI) | Academia Lab: Evilevitch Lab, Lund University Pain point: 5-year scientific impasse; <0.1% of sample data usable per experiment due to manual workflow Result: Processed ~100% of samples, identified hidden cell-line defense mechanism, progressed more in one month than in five years. Now collecting large-scale viral latency data. Quote: "Finally, we feel like this project is actually working… We're collecting large-scale data and unraveling mechanisms of viral latency, something that just wasn't possible before." Video: https://www.youtube.com/watch?v=DAbxkRfxwL8 #5 — DNA Damage / Genome Integrity | Academia Lab: Genome Integrity Lab Pain point: Considering purchasing a $500k high-content screener for new lab Result: Avoided $500k hardware purchase; achieved HCS-grade analysis on existing standard microscope Link: https://www.cytely.io/case-studies/genome-integrity-lab-dna-damage #6 — Cancer Mechanobiology | Academia Lab: Swaminathan Lab, Lund University Pain point: Manual fibroblast morphology analysis limited to small sample sizes Result: 10x more cells analyzed per condition, 75% reduction in imaging/analysis time, discovered novel multi-parameter correlations Link: https://www.cytely.io/case-studies/swaminathan-lab-cancer-mechanobiology #7 — Muscular Dystrophy | Academia Lab: European Muscular Dystrophy Research Lab Pain point: Days of manual muscle fiber counting per sample Result: 99.9% accuracy vs manual counting, 3,500+ fibers per sample, days→minutes Link: https://www.cytely.io/case-studies/muscular-dystrophy-research #8 — Phagocytosis Screening | Academia Lab: Immunology Lab, Lund University Pain point: Flow cytometry producing false positives; no way to visually verify mechanism of action Result: Caught artifacts flow cytometry missed, linked binding to function with visual proof, eliminated bioinformatics bottleneck Link: https://www.cytely.io/case-studies/immunology-lab-phagocytosis-screening #9 — Conducting Polymer Biocompatibility | Academia Lab: Organic Electronics Lab Pain point: No existing tool could analyze their conducting polymer samples Result: Unblocked a workflow no other tool could solve, enabled 96-well plate screening Link: https://www.cytely.io/case-studies/organic-electronics-lab-polymer-analysis #10 — Viral Oncogenesis / Co-localization | Academia Lab: Viral Oncogenesis Lab (Finland) Pain point: Needed custom co-localization workflow fast, no coding expertise Result: 30 min to set up pipeline, immediate publication-ready results, no coding needed Link: https://www.cytely.io/case-studies/viral-oncogenesis