Imaging Mass Cytometry for In Situ Immune Profiling.

The complexities and cellular heterogeneity associated with tissues necessitate the concurrent detection of markers beyond the limitations of conventional imaging approaches in order to spatially resolve the relationships between immune cell populations and their environments. This is a necessary complement to single-cell suspension-based methods to inform a better understanding of the events that may underlie pathological conditions. Imaging mass cytometry is a high-dimensional imaging modality that allows for the concurrent detection of up to 40 protein markers of interest across tissues at subcellular resolution. Here, we present an optimized staining protocol for imaging mass cytometry with modifications that integrate RNAscope. This unique addition enables combined protein and single-molecule RNA detection, thereby expanding the utility of imaging mass cytometry to researchers investigating low abundance or noncoding targets. In general, the procedure described is broadly applicable for comprehensive immune profiling of host-pathogen interactions, tumor microenvironments and inflammatory conditions, all within the tissue contexture.

OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration.

Analysis of imaging mass cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single-cell segmentation and suboptimal approaches for data visualization and exploration. This can lead to inaccurate identification of cell phenotypes, states, or spatial relationships compared to reference data from single-cell suspension technologies. To this end we have developed the "OPTimized Imaging Mass cytometry AnaLysis (OPTIMAL)" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualization/clustering, and spatial neighborhood analysis. Using a panel of 27 metal-tagged antibodies recognizing well-characterized phenotypic and functional markers to stain the same Formalin-Fixed Paraffin Embedded (FFPE) human tonsil sample tissue microarray over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, 5 different dimensionality reduction algorithms, and 2 clustering methods. Finally, we assessed the optimal approach for performing neighborhood analysis. We found that single-cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bivariate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximized the statistical separation between negative and positive signal distributions and a simple Z-score normalization step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing phenograph in terms of cell type identification. We also found that neighborhood analysis was influenced by the method used for finding neighboring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly, OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output and allows for single-cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.

DNA-ICM as an adjuvant method applied on oral cytological specimens.

OBJECTIVE: This study aimed to evaluate cytology diagnosis accuracy using adjuvant methods in clinical routine for oral cancer. STUDY DESIGN: This prospective study was conducted on 98 patients with clinically potentially malignant or malignant oral cavity lesions. One oral lesion smear was taken from each patient using a cytobrush before biopsy and stored at PreservCyt Thinprep. Samples were cytologically analyzed, and DNA ploidy measurement was performed on the same slide. The diagnostic methods' accuracy was then calculated. RESULTS: In clinical inspection, 61 patients had suspicious lesions for malignancy, whereas 37 had potentially malignant disorders. Cytology associated with DNA image cytometry presented a sensitivity of 81.2% and specificity of 90.9%. When analyzing lesions located in high-risk sites to oral malignancies individually, cytology associated with DNA image cytometry presented a sensitivity of 88.2%, specificity of 100.0%, accuracy of 90.0%, and Kappa value of 0.77 (CI 95%: 0.48-1.00). CONCLUSIONS: Association between cytology and DNA image cytometry is an objective and non-invasive diagnostic method that demonstrated high sensitivity and specificity in diagnosing malignant epithelial squamous cell transformation in the oral cavity.

A novel process for H&E, immunofluorescence, and imaging mass cytometry on a single slide with a concise analytics pipeline.

Imaging mass cytometry (IMC) is a powerful spatial technology that utilizes cytometry time of flight to acquire multiplexed image datasets with up to 40 markers, via metal-tagged antibodies. Recent advances in IMC have led to the inclusion of RNAScope probes and multiple new analysis pipelines have led to faster analyses and better results. However, IMC still suffers from lower resolution (1 µm2 pixels) and relatively small regions of interest (ROIs) (<2 mm2 ) compared to other, light-based microscope technologies. Capturing higher-resolution images on serial sections causes great difficulty when attempting to align cells and structures across serial sections, especially when observing smaller cell types and structures. Therefore, we demonstrate the combination of H&E and multiplex immunofluorescence imaging, for much higher resolution of the structural and cellular compartments found throughout the entire tissue section, with the high-dimensionality of IMC for specific ROIs on a single slide. Additionally, we demonstrate a simple and effective open-source cell segmentation and IMC analysis pipeline with previously published and freely available software.

High-throughput method to analyze the cytotoxicity of CAR-T Cells in a 3D tumor spheroid model using image cytometry.

Solid tumors account for approximately 90% of all adult human cancers. As such, the development of novel cellular therapies has become of increasing importance to target solid tumor malignancies, such as prostate, lung, breast, bladder, colon, and liver cancers. One such cellular therapy relies on the use of chimeric antigen receptor T cells (CAR-T cells). CAR-T cells are engineered to target specific antigens on tumor cells. To date, there are six FDA-approved CAR-T cell therapies that have been utilized for hematologic B cell malignancies. Immune cell trafficking and immunosuppressive factors within the tumor microenvironment increase the relative difficulty in developing a robust CAR-T cell therapy against solid tumors. Therefore, it is critical to develop novel methodologies for high-throughput phenotypic and functional assays using 3D tumor spheroid models to assess CAR-T cell products against solid tumors. In this manuscript, we discuss the use of CAR-T cells targeted towards PSMA, an antigen that is found on prostate cancer tumor cells, the second most common cause of cancer deaths among men worldwide. We demonstrate the use of high-throughput, plate-based image cytometry to characterize CAR-T cell-mediated cytotoxic potency against 3D prostate tumor spheroids. We were able to kinetically evaluate the efficacy and therapeutic value of PSMA CAR-T cells by analyzing the cytotoxicity against prostate tumor spheroids. In addition, the CAR-T cells were fluorescently labeled to visually identify the location of the T cells as cytotoxicity occurs, which may provide more meaningful information for assessing the functionality of the CAR-T cells. The proposed image cytometry method can overcome limitations placed on traditional methodologies to effectively assess cell-mediated 3D tumor spheroid cytotoxicity and efficiently generate time- and dose-dependent results.

Single-cell high-dimensional imaging mass cytometry: one step beyond in oncology.

Solid tumors have a dynamic ecosystem in which malignant and non-malignant (endothelial, stromal, and immune) cell types constantly interact. Importantly, the abundance, localization, and functional orientation of each cell component within the tumor microenvironment vary significantly over time and in response to treatment. Such intratumoral heterogeneity influences the tumor course and its sensitivity to treatments. Recently, high-dimensional imaging mass cytometry (IMC) has been developed to explore the tumor ecosystem at the single-cell level. In the last years, several studies demonstrated that IMC is a powerful tool to decipher the tumor complexity. In this review, we summarize the potential of this technology and how it may be useful for cancer research (from preclinical to clinical studies).

Artificial intelligence-based classification of peripheral blood nucleated cells using label-free imaging flow cytometry.

Label-free image identification of circulating rare cells, such as circulating tumor cells within peripheral blood nucleated cells (PBNCs), the vast majority of which are white blood cells (WBCs), remains challenging. We previously described developing label-free image cytometry for classifying live cells using computer vision technology for pattern recognition, based on the subcellular structure of the quantitative phase microscopy images. We applied our image recognition methods to cells flowing in a flow cytometer microfluidic channel, and differentiated WBCs from cancer cell lines (area under receiver operating characteristic curve = 0.957). We then applied this method to healthy volunteers' and advanced cancer patients' blood samples and found that the non-WBC fraction rates (NWBC-FRs), defined as the percentage of cells classified as non-WBCs of the total PBNCs, were significantly higher in cancer patients than in healthy volunteers. Furthermore, we monitored NWBC-FRs over the therapeutic courses in cancer patients, which revealed the potential ability in monitoring the clinical status during therapy. Our image recognition system has the potential to provide a morphological diagnostic tool for circulating rare cells as non-WBC fractions.

Use of DNA image cytometry in conducting oral cancer screening in rural India.

OBJECTIVES: Oral cancer screening can assist in the early detection of oral potentially malignant lesions (OPMLs) and prevention of oral cancers. It can be challenging for clinicians to differentiate OPMLs from benign conditions. Adjunct screening tools such as fluorescence visualisation (FV) and DNA image cytometry (DNA-ICM) have shown success in identifying OPMLs in high-risk clinics. For the first time we aimed to assess these technologies in Indian rural settings and evaluate if these tools helped clinicians identify high-risk lesions during screening. METHODS: Dental students and residents screened participants in five screening camps held in villages outside of Hyderabad, India, using extraoral, intraoral, and FV examinations. Lesion and normal tissue brushings were collected for DNA-ICM analysis and cytology. RESULTS: Of the 1116 participants screened, 184 lesions were observed in 152 participants. Based on white light examination (WLE), 45 lesions were recommended for biopsy. Thirty-five were completed on site; 25 (71%) were diagnosed with low-grade dysplasias (17 mild, 8 moderate) and the remaining 10 showed no signs of dysplasia. FV loss was noted in all but one dysplastic lesion and showed a sensitivity of 96% and specificity of 17%. Cytology combined with DNA-ICM had a sensitivity of 64% and specificity of 86% in detecting dysplasia. CONCLUSION: DNA-ICM combined with cytology identified the majority of dysplastic lesions and identified additional lesions, which were not considered high-risk during WLE and biopsy on site. Efforts to follow-up with these participants are ongoing. FV identified most high-risk lesions but added limited value over WLE.

Application of High-Throughput Imaging Mass Cytometry Hyperion in Cancer Research.

Imaging mass cytometry (IMC) enables the in situ analysis of in-depth-phenotyped cells in their native microenvironment within the preserved architecture of a single tissue section. To date, it permits the simultaneous analysis of up to 50 different protein- markers targeted by metal-conjugated antibodies. The application of IMC in the field of cancer research may notably help 1) to define biomarkers of prognostic and theragnostic significance for current and future treatments against well-established and novel therapeutic targets and 2) to improve our understanding of cancer progression and its resistance mechanisms to immune system and how to overcome them. In the present article, we not only provide a literature review on the use of the IMC in cancer-dedicated studies but we also present the IMC method and discuss its advantages and limitations among methods dedicated to deciphering the complexity of cancer tissue.

DNA image cytometry parameters to identify high-grade cervical lesions.

OBJECTIVE: Evaluate the performance of different DNA image cytometry (DNA-ICM) ploidy parameters in the categorisation of DNA-ICM results and identification of high-grade cervical intraepithelial neoplasia or worse (≥ CIN2). METHODS: Cervical samples from 232 women were collected for DNA-ICM analysis and biopsy confirmation. Five DNA parameters were used to define DNA aneuploidy: number of cells with exceeding events (EE) over 2.5cEE, 4cEE, 5cEE and 9cEE, and aneuploid stemlines. DNA-ICM results were categorised as normal, suspicious, and abnormal. RESULTS: For individual DNA ploidy parameters, sensitivity values for 50 cells with 2.5cEE, 45 cells with 4cEE, 1 cell with 9cEE and aneuploid stemline were 72.95%. 54.1%, 69.67% and 54.1%, while specificity values were 80.0%, 90.0%, 89.09% and 95.45%, respectively. For the 5cEE parameter, the sensitivity values for 1, 2, 3, 4 and 5 cells were 93.44%, 85.25%, 81.97%, 77.87% and 75.41%, while specificity values were 46.36%, 63.64%, 74.55%, 76.36% and 80.91%, respectively. For categorised DNA-ICM results, a suspicious result showed superior sensitivity than an abnormal result (87.70% vs 82.79%, P = 0.031), but lower specificity (54.55% vs 75.45%, P < 0.001). Both types of DNA-ICM result were statistically significantly different from a normal result (P < 0.05). CONCLUSION: For prognostic purposes, 1 cell with 9cEE, 45 cells with 4cEE and aneuploid stemline are the best parameters with which to categorise an abnormal DNA-ICM result, followed by 50 cells with 2.5cEE and 4 cells with 5cEE. For screening purposes, 10 cells with 2.5cEE, 10 cells with 4cEE, and 2 cells with 5cEE are suitable parameters with which to categorise a suspicious DNA-ICM result.

Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment.

A holistic understanding of tissue and organ structure and function requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) enables simultaneous detection of up to 40 antigens and transcripts using metal-tagged antibodies but has so far been restricted to two-dimensional imaging. Here we report the development of 3D IMC for multiplexed 3D tissue analysis at single-cell resolution and demonstrate the utility of the technology by analysis of human breast cancer samples. The resulting 3D models reveal cellular and microenvironmental heterogeneity and cell-level tissue organization not detectable in two dimensions. 3D IMC will prove powerful in the study of phenomena occurring in 3D space such as tumor cell invasion and is expected to provide invaluable insights into cellular microenvironments and tissue architecture.

Automated Assessment of Cancer Drug Efficacy On Breast Tumor Spheroids in Aggrewell™400 Plates Using Image Cytometry.

Tumor spheroid models have proven useful in the study of cancer cell responses to chemotherapeutic compounds by more closely mimicking the 3-dimensional nature of tumors in situ. Their advantages are often offset, however, by protocols that are long, complicated, and expensive. Efforts continue for the development of high-throughput assays that combine the advantages of 3D models with the convenience and simplicity of traditional 2D monolayer methods. Herein, we describe the development of a breast cancer spheroid image cytometry assay using T47D cells in Aggrewell™400 spheroid plates. Using the Celigo® automated imaging system, we developed a method to image and individually track thousands of spheroids within the Aggrewell™400 microwell plate over time. We demonstrate the use of calcein AM and propidium iodide staining to study the effects of known anti-cancer drugs Doxorubicin, Everolimus, Gemcitabine, Metformin, Paclitaxel and Tamoxifen. We use the image cytometry results to quantify the fluorescence of calcein AM and PI as well as spheroid size in a dose dependent manner for each of the drugs. We observe a dose-dependent reduction in spheroid size and find that it correlates well with the viability obtained from the CellTiter96® endpoint assay. The image cytometry method we demonstrate is a convenient and high-throughput drug-response assay for breast cancer spheroids under 400 µm in diameter, and may lay a foundation for investigating other three-dimensional spheroids, organoids, and tissue samples.

TITAN: An end-to-end data analysis environment for the Hyperion™ imaging system.

Imaging Mass Cytometry (IMC) is a powerful high-throughput technique enabling resolution of up to 37 markers in a single fixed tissue section while also preserving in situ spatial relationships. Currently, IMC processing and analysis necessitates the use of multiple different software, labour-intensive pipeline development, different operating systems and knowledge of bioinformatics, all of which are a barrier to many potential users. Here we present TITAN - an open-source, single environment, end-to-end pipeline that can be utilized for image visualization, segmentation, analysis and export of IMC data. TITAN is implemented as an extension within the publicly available 3D Slicer software. We demonstrate the utility, application, reliability and comparability of TITAN using publicly available IMC data from recently-published breast cancer and COVID-19 lung injury studies. Compared with current IMC analysis methods, TITAN provides a user-friendly, efficient single environment to accurately visualize, segment, and analyze IMC data for all users.

Rapid cell counting and viability detection method of Escherichia coli Nissle using image cytometry.

The improvement of cell enumeration methods for the counting of Escherichia coli (E. coli) is important as E. coli gains in popularity as a basis for biopharmaceutical applications. In the biopharmaceutical industry, enumerating, characterizing, and dosing the accurate number of cells is imperative. In this work, we demonstrate the utilization of a chip-based image cytometer using a thin-gap, low volume counting chamber consumable to directly enumerate E. coli in bright field and fluorescence, and measure their viability using SYTOX™ Green. The total E. coli particles can be counted accurately label-free by adjusting the focus and targeting the linear range of the instrument. The E. coli are stained with SYTOX™ Green to count the membrane-compromised dead bacterial cells in the green fluorescence channel, while the total cells are counted using the bright field channel. Optimization of the system settings, image focus, cell counting range, and staining conditions have yielded a precise, rapid, and accurate E. coli cell enumeration and viability measurement.

Aptamer Probes Labeled with Lanthanide-Doped Carbon Nanodots Permit Dual-Modal Fluorescence and Mass Cytometric Imaging.

High-dimensional imaging mass cytometry (IMC) enables simultaneous quantification of over 35 biomarkers on one tissue section. However, its limited resolution and ultralow acquisition speed remain major issues for general clinical application. Meanwhile, conventional immunofluorescence microscopy (IFM) allows sub-micrometer resolution and rapid identification of the region of interest (ROI), but only operates with low multiplicity. Herein, a series of lanthanide-doped blue-, green-, and red-fluorescent carbon nanodots (namely, B-Cdots(Ln1 ), G-Cdots(Ln2 ), and R-Cdots(Ln3 )) as fluorescence and mass dual-modal tags are developed. Coupled with aptamers, B-Cdots(159 Tb)-A10-3.2, G-Cdots(165 Ho)-AS1411, and R-Cdots(169 Tm)-SYL3C dual-functional aptamer probes, which are then multiplexed with commercially available Maxpar metal-tagged antibodies for analyzing clinical formalin-fixed, paraffin-embedded (FFPE) prostatic adenocarcinoma (PaC) tissue, are further synthesized. The rapid identification of ROI with IFM using fluorescence signals and subsequent multiplexed detection of in situ ROI with IMC using the same tissue section is demonstrated. Dual-modal probes save up to 90% IMC blind scanning time for a standard 3.5 mm × 3.5 mm overall image. Meanwhile, the IFM provides refined details and topological spatial distributions for the functional proteins at optical resolution, which compensates for the low resolution of the IMC imaging.

Characterisation of tumour microenvironment remodelling following oncogene inhibition in preclinical studies with imaging mass cytometry.

Mouse models are critical in pre-clinical studies of cancer therapy, allowing dissection of mechanisms through chemical and genetic manipulations that are not feasible in the clinical setting. In studies of the tumour microenvironment (TME), multiplexed imaging methods can provide a rich source of information. However, the application of such technologies in mouse tissues is still in its infancy. Here we present a workflow for studying the TME using imaging mass cytometry with a panel of 27 antibodies on frozen mouse tissues. We optimise and validate image segmentation strategies and automate the process in a Nextflow-based pipeline (imcyto) that is scalable and portable, allowing for parallelised segmentation of large multi-image datasets. With these methods we interrogate the remodelling of the TME induced by a KRAS G12C inhibitor in an immune competent mouse orthotopic lung cancer model, highlighting the infiltration and activation of antigen presenting cells and effector cells.

New concepts on immunology of Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory and immune-driven demyelinating disease of the central nervous system (CNS). During the past decade, major advances have been made to understand the development of MS as well as its progressive stage. Here, we discuss some emerging concepts on immunology of MS, including the growing interest in the involvement of gut microbiota and the recent pathological concepts on the progression phase. Finally, we present some immuno-tools recently available that contribute to better understand diversity and function of the immune system.

Spatial UMAP and Image Cytometry for Topographic Immuno-oncology Biomarker Discovery.

Multiplex immunofluorescence (mIF) can detail spatial relationships and complex cell phenotypes in the tumor microenvironment (TME). However, the analysis and visualization of mIF data can be complex and time-consuming. Here, we used tumor specimens from 93 patients with metastatic melanoma to develop and validate a mIF data analysis pipeline using established flow cytometry workflows (image cytometry). Unlike flow cytometry, spatial information from the TME was conserved at single-cell resolution. A spatial uniform manifold approximation and projection (UMAP) was constructed using the image cytometry output. Spatial UMAP subtraction analysis (survivors vs. nonsurvivors at 5 years) was used to identify topographic and coexpression signatures with positive or negative prognostic impact. Cell densities and proportions identified by image cytometry showed strong correlations when compared with those obtained using gold-standard, digital pathology software (R2 > 0.8). The associated spatial UMAP highlighted "immune neighborhoods" and associated topographic immunoactive protein expression patterns. We found that PD-L1 and PD-1 expression intensity was spatially encoded-the highest PD-L1 expression intensity was observed on CD163+ cells in neighborhoods with high CD8+ cell density, and the highest PD-1 expression intensity was observed on CD8+ cells in neighborhoods with dense arrangements of tumor cells. Spatial UMAP subtraction analysis revealed numerous spatial clusters associated with clinical outcome. The variables represented in the key clusters from the unsupervised UMAP analysis were validated using established, supervised approaches. In conclusion, image cytometry and the spatial UMAPs presented herein are powerful tools for the visualization and interpretation of single-cell, spatially resolved mIF data and associated topographic biomarker development.

Development of an Image-Based HCS-Compatible Method for Endothelial Barrier Function Assessment.

The recent renascence of phenotypic drug discovery (PDD) is catalyzed by its ability to identify first-in-class drugs and deliver results when the exact molecular mechanism is partially obscure. Acute respiratory distress syndrome (ARDS) is a severe, life-threatening condition with a high mortality rate that has increased in frequency due to the COVID-19 pandemic. Despite decades of laboratory and clinical study, no efficient pharmacological therapy for ARDS has been found. An increase in endothelial permeability is the primary event in ARDS onset, causing the development of pulmonary edema that leads to respiratory failure. Currently, the detailed molecular mechanisms regulating endothelial permeability are poorly understood. Therefore, the use of the PDD approach in the search for efficient ARDS treatment can be more productive than classic target-based drug discovery (TDD), but its use requires a new cell-based assay compatible with high-throughput (HTS) and high-content (HCS) screening. Here we report the development of a new plate-based image cytometry method to measure endothelial barrier function. The incorporation of image cytometry in combination with digital image analysis substantially decreases assay variability and increases the signal window. This new method simultaneously allows for rapid measurement of cell monolayer permeability and cytological analysis. The time-course of permeability increase in human pulmonary artery endothelial cells (HPAECs) in response to the thrombin and tumor necrosis factor α treatment correlates with previously published data obtained by transendothelial resistance (TER) measurements. Furthermore, the proposed image cytometry method can be easily adapted for HTS/HCS applications.

Image3C, a multimodal image-based and label-independent integrative method for single-cell analysis.

Image-based cell classification has become a common tool to identify phenotypic changes in cell populations. However, this methodology is limited to organisms possessing well-characterized species-specific reagents (e.g., antibodies) that allow cell identification, clustering, and convolutional neural network (CNN) training. In the absence of such reagents, the power of image-based classification has remained mostly off-limits to many research organisms. We have developed an image-based classification methodology we named Image3C (Image-Cytometry Cell Classification) that does not require species-specific reagents nor pre-existing knowledge about the sample. Image3C combines image-based flow cytometry with an unbiased, high-throughput cell clustering pipeline and CNN integration. Image3C exploits intrinsic cellular features and non-species-specific dyes to perform de novo cell composition analysis and detect changes between different conditions. Therefore, Image3C expands the use of image-based analyses of cell population composition to research organisms in which detailed cellular phenotypes are unknown or for which species-specific reagents are not available.

Cells are the building blocks of all living organisms. They come in many types, each with a different role. Understanding the composition of cells, i.e., how many cells and which types of cells are present inside an organ can indicate what that organ does. It can also reveal how that organ changes under different conditions, like during an infection or treatment. The most powerful methods for studying cells work well for species researchers already know a lot about, such as mice, zebrafish or humans, but not for less studied animals. To change this Accorsi, Box, Peuß et al. created a new tool called Image3C to be used for studying the composition of cells in less researched organisms. Instead of using reagents that only work for specific species, the tool uses molecules that work across many species, like dyes that stain the cell nucleus. A cell-sorting machine, known as a flow cytometer, connected to a microscope then takes pictures of hundreds of stained cells each second and Image3C groups them based on their appearance, without the need for any prior knowledge about the cell types. Accorsi et al. then tested Image3C on immune system cells of zebrafish, a well-studied animal, and apple snails, an under-studied animal. For both species, the tool was able to sort cells into groups representing different parts of the immune system. Image3C speeds up the grouping process and reduces the need for user intervention and time. This lowers the risk of bias compared to manual counting of cells. It can sort cells even when the types of cells in an organism are unknown and even when specialized reagents for an organism do not exist. This means that it could characterise the cell make-up of new tissues coming from organisms never studied before. Access to this uncharted world of cells stands to reveal previously inaccessible clues about how organs behave and evolve and allow researchers to investigate the impact of environmental changes on these cells.