Imaging Flow Cytometry Enhances Characterization of Immune Cell Phenotype and Function 2255710

Developing a Label-Free High-Throughput Flow Assay by Imaging Cytometry on the Attune CytPix 2300698

Flow cytometry is a primary tool for characterizing immune cell dynamics in high-throughput screening (HTS) assays. However, traditional staining and acquisition procedures demand substantial antibodies, resources, and time. Moreover, HTS assays are often constrained to a minimal set of markers, limiting biological readouts to largely binary outputs (e.g., activated vs. resting) and oversimplifying the complexity of drug responses. To simplify assays while improving biological insights, we leveraged the imaging capabilities of the Attune CytPix to investigate whether imaging flow cytometry can replace activation and viability markers and detect complex phenotypes based on cellular morphology. Using brightfield imaging-derived parameters and UMAP dimensionality reduction analysis, we successfully resolved distinct clusters separating human resting Peripheral Blood Mononuclear Cells (PBMC) from activated CD25+CD69+ PBMCs. Additionally, cell debris and morphologically distinct dead cells formed separate populations, suggesting the ability to differentiate modalities of cell death through imaging alone. We then applied an AI-driven Python-based analysis to the CytPix derived images, and successfully reconstructed label-free drug-response curves comparable to those derived via conventional manual gating in standard flow cytometry. Building on these findings, we are now integrating additional imaging-derived parameters to uncover phenotypes that may not be captured by conventional marker-based approaches, with the goal of enabling richer, label-free HTS readouts at scale. n/a Technological Innovations in Immunology (TECH)

Imaging flow cytometry combined with spatial light modulator

Imaging flow cytometry (IFC), an instrument that combines the features of microscopy with image acquisition and microfluidic chips, enables the rapid analysis of single-cell image information at high throughput. Currently, imaging flow cytometers have been integrated with deep learning for classification purposes. However, due to the rich information contained in images, deep learning networks require complex calculations, often resulting in lengthy computation times and substantial energy consumption. In this work, we propose a method that integrates optical neural network (ONN) to enhance classification efficiency in imaging flow cytometry while reducing computational complexity and processing time. We introduce a spatial light modulator (SLM) into the optical system of the imaging flow cytometer to preprocess the forward scattering signals of cells within the optical neural network framework, thereby lowering the subsequent computational burden on images of cells. A comparison of bright-field images of HepG2 and SW480 cells, along with the SLM-processed images, demonstrates a significant improvement in both computational efficiency and accuracy. Following training of 400 samples and 1,000 training epochs, the model achieved an accuracy of 96%.

Iterative multiplex imaging cytometry for cross-species analyses of in situ tissue leukocytes

Traditional immunohistochemistry techniques are limited in the number of fluorescent detection channels, markers, and staining resolution, which restricts the ability to fully evaluate bulk tissue samples and biopsies. Recent technical advancements, such as co-detection by indexing and spatial proteomics, have enabled in situ visualization of tissues by combining multiplex assays, repetitive staining, and quantitative image analysis. These technologies can identify cellular co-expression, cellular spatial relationships, tissue heterogeneity, and detect low-abundance molecules, which are critical for basic immunology, disease evaluation, and therapeutic evaluation studies. However, these methods require specially conjugated antibodies and protocols to analyze highly multiplexed tissue imaging (HMTI) readouts. Here, we utilize imaging cytometry as a viable alternative that also enables individual cellular cytometry analyses in two-dimensional formats. This technique has been used to investigate human and mouse tissues but is underexplored in the translationally relevant rhesus macaque (RM) model. Here, we demonstrate the use of this platform to image RM placenta, jejunum, kidney, and liver stored in OCT, using commercially available fluorophore-conjugated antibodies to identify structural markers (cytokeratin and vimentin), pan-immune cells (CD45), T cells (CD3 and CD8), natural killer cells (NKG2A/C), monocytes (CD16) and macrophages (CD68 and CD163), without any customization. We compared these preclinical samples to human samples to emphasize the potential for cross-species translational analyses using this platform. The flexibility to perform multiple rounds of photobleaching and fluorophore-based staining, combined with the ability to compensate for autofluorescence, makes this technology extremely valuable for deciphering tissue architecture and the spatial distribution of immune cells. Furthermore, we leveraged the platform’s ability to export data in HMTI format and flow cytometry standard format, compatible with other quantitative downstream analysis pipelines, to simultaneously visualize the spatial distribution of various cell populations.

Hybrid Cytometry: automated live-cell tracking with a final snapshot of fixed-cell phenotyping to study immune-synapse dynamics 2263890

Even advanced flow cytometry and fluorescence microscopy fall short in linking dynamic immune cell functions and phenotypes at high plexity. We aimed to develop an automated workflow that bridges this gap, capturing mixed culture dynamics with deep phenotyping and optional integration with high-resolution imaging or single-cell RNA sequencing, tracking individual cells throughout the experiment. We implemented an imaging-based workflow integrating automated liquid handling, fluorescence whole-plate scanning, and tracking of sequential activation cycles in live cells, together with corresponding cytokine or surface marker expression in fixed cells. Datasets were computationally aligned to correlate up to 3-plex dynamic functional readouts with phenotypic features. A plexity of 12—20 markers can be achieved through sequential immunostaining. This centrifugation-free pericellular liquid exchange workflow enables us to: • Track individual T cells through repeated peptide stimulations of human PBMCs; • Validate activation by downstream cytokine or surface marker staining; • Increase per sample data yield up to 20—100-fold versus ELISpot or flow cytometry; • Visualize transient immune synapses and interactions including antigen presentation, cross-presentation, T-cell—B-cell cooperation, dynamic killing, and Teff—Treg proximity. This method provides a scalable, high-content approach for low-volume, single-cell immunology supporting target discovery, vaccine design, and mechanistic research. 100XBIO Inc. (Private Investment Funding) Technological Innovations in Immunology (TECH)

Relationship between CAR spatial organization and CAR T cell function revealed by imaging flow cytometry and transcriptomic profiling 2260438

These findings suggest CAR spatial organization correlates with functional states and demonstrate the power of integrating advanced flow and imaging cytometry to resolve CAR T cell phenotypes and spatial dynamics with high precision.

Imaging mass cytometry