Unlabeled Cells Research Articles

Preclinical and Clinical-Scale Magnetic Particle Imaging of Natural Killer Cells: in vitro and ex vivo Demonstration of Cellular Sensitivity, Resolution, and Quantification.

Clinical adoption of NK cell immunotherapy is underway for medulloblastoma and osteosarcoma, however there is currently little feedback on cell fate after administration. We propose magnetic particle imaging (MPI) may have applications for the quantitative detection of NK cells. Human-derived NK-92 cells were labeled by co-incubation with iron oxide nanoparticles (VivoTrax™) for 24h then excess nanoparticles were washed with centrifugation. Cytolytic activity of labeled versus unlabeled NK-92 cells was assessed after 4h of co-incubation with medulloblastoma cells (DAOY) or osteosarcoma cells (LM7 or OS17). Labeled NK-92 cells at two different doses (0.5 or 1 × 106) were administered to excised mouse brains (cerebellum), fibulas, and lungs then imaged by 3D preclinical MPI (MOMENTUM™) for detection relative to fiducial markers. NK-92 cells were also imaged by clinical-scale MPI under development at Magnetic Insight Inc. NK-92 cells were labeled with an average of 3.17 pg Fe/cell with no measurable effects on cell viability or cytolytic activity against 3 tumor cell lines. MPI signal was directly quantitative with the number of labeled NK-92 cells, with preclinical limit of detection of 3.1 × 104 cells on MOMENTUM imager. Labeled NK-92 cells could be accurately localized in mouse brains, fibulas, and lungs within < 1mm of stereotactic injection coordinates with preclinical scanner. Feasibility for detection on a clinical-scale MPI scanner was demonstrated using 4 × 107 labeled NK-92 cells, which is in the range of NK cell doses administered in our previous clinical trial. MPI can provide sensitive, quantitative, and accurate spatial information on NK cells soon after delivery, showing initial promise to address a significant unmet clinical need to track NK cell fate in patients.

Comparison of the dosimetry and cell survival effect of 177Lu and 161Tb somatostatin analog radiopharmaceuticals in cancer cell clusters and micrometastases

Background177Lu-based radiopharmaceuticals (RPs) are the most used for targeted radionuclide therapy (TRT) due to their good response rates. However, the worldwide availability of 177Lu is limited. 161Tb represents a potential alternative for TRT, as it emits photons for SPECT imaging, β−-particles for therapy, and also releases a significant yield of internal conversion (IE) and Auger electrons (AE). This research aimed to evaluate cell dosimetry with the MIRDcell code considering a realistic localization of three 161Tb- and 177Lu-somatostatin (SST) analogs in different subcellular regions as reported in the literature, various cell cluster sizes (25–1000 µm of radius) and percentage of labeled cells. Experimental values of the α- and β-survival coefficients determined by external beam photon irradiation were used to estimate the survival fraction (SF) of AR42J pancreatic cell clusters and micrometastases.ResultsThe different localization of RPs labeled with the same radionuclide within the cells, resulted in only slight variations in the dose absorbed by the nuclei (ADN) of the labeled cells with no differences observed in either the unlabeled cells or the SF. ADN of labeled cells (MDLC) produced by 161Tb-RPs were from 2.8–3.7 times higher than those delivered by 177Lu-RPs in cell clusters with a radius lower than 0.1 mm and 10% of labeled cells, due to the higher amount of energy emitted by 161Tb-disintegration in form of IE and AE. However, the 161Tb-RPs/177Lu-RPs MDLC ratio decreased below 1.6 in larger cell clusters (0.5–1 mm) with > 40% labeled cells, due to the significantly higher 177Lu-RPs cross-irradiation contribution. Using a fixed number of disintegrations, SFs of 161Tb-RPs in clusters with > 40% labeled cells were lower than those of 177Lu-RPs, but when the same amount of emitted energy was used no significant differences in SF were observed between 177Lu- and 161Tb-RPs, except for the smallest cluster sizes.ConclusionsDespite the emissions of IE and AE from 161Tb-RPs, their localization within different subcellular regions exerted a negligible influence on the ADN. The same cell damage produced by 177Lu-RPs could be achieved using smaller quantities of 161Tb-RPs, thus making 161Tb a suitable alternative for TRT.

Probing chromatin condensation dynamics in live cells using interferometric scattering correlation spectroscopy

Chromatin organization and dynamics play important roles in governing the regulation of nuclear processes of biological cells. However, due to the constant diffusive motion of chromatin, examining chromatin nanostructures in living cells has been challenging. In this study, we introduce interferometric scattering correlation spectroscopy (iSCORS) to spatially map nanoscopic chromatin configurations within unlabeled live cell nuclei. This label-free technique captures time-varying linear scattering signals generated by the motion of native chromatin on a millisecond timescale, allowing us to deduce chromatin condensation states. Using iSCORS imaging, we quantitatively examine chromatin dynamics over extended periods, revealing spontaneous fluctuations in chromatin condensation and heterogeneous compaction levels in interphase cells, independent of cell phases. Moreover, we observe changes in iSCORS signals of chromatin upon transcription inhibition, indicating that iSCORS can probe nanoscopic chromatin structures and dynamics associated with transcriptional activities. Our scattering-based optical microscopy, which does not require labeling, serves as a powerful tool for visualizing dynamic chromatin nano-arrangements in live cells. This advancement holds promise for studying chromatin remodeling in various crucial cellular processes, such as stem cell differentiation, mechanotransduction, and DNA repair.

InVivo PET Imaging of 89Zr-Labeled Natural Killer Cells and the Modulating Effects of a Therapeutic Antibody.

Natural killer (NK) cells can kill cancer cells via antibody-dependent cell-mediated cytotoxicity (ADCC): a tumor-associated IgG antibody binds to the Fcγ receptor CD16 on NK cells via the antibody Fc region and activates the cytotoxic functions of the NK cell. Here, we used PET imaging to assess NK cell migration to human epidermal growth factor receptor 2 (HER2)-positive HCC1954 breast tumors, examining the influence of HER2-targeted trastuzumab antibody treatment on NK cell tumor accumulation. Methods: Human NK cells from healthy donors were expanded ex vivo and labeled with [89Zr]Zr-oxine. In vitro experiments compared the phenotypic markers, viability, proliferation, migration, degranulation, and ADCC behaviors of both labeled (89Zr-NK) and unlabeled NK cells. Female mice bearing orthotopic human breast HCC1954 tumors were administered 89Zr-NK cells alongside trastuzumab treatment or a sham treatment and then scanned using PET/CT imaging over 7 d. Flow cytometry and γ-counting were used to analyze the presence of 89Zr-NK cells in liver and spleen tissues. Results: 89Zr cell radiolabeling yields measured 42.2% ± 8.0%. At an average specific activity of 16.7 ± 4.7 kBq/106 cells, 89Zr-NK cells retained phenotypic and functional characteristics including CD56 and CD16 expression, viability, migration, degranulation, and ADCC capabilities. In vivo PET/CT studies indicated predominant accumulation of 89Zr-NK cells in the liver and spleen. Ex vivo analyses of liver and spleen tissues indicated that the administered human 89Zr-NK cells retained their radioactivity invivo and that 89Zr did not transfer to cells of murine soft tissues, thus validating this 89Zr PET method for NK cell tracking. Notably, 89Zr-NK cells migrated to HER2-positive tumors, both with and without trastuzumab treatment. Trastuzumab treatment was associated with an increased 89Zr-NK cell signal at days 1 and 3 after injection. Conclusion: In vitro, 89Zr-NK cells maintained key cellular and cytotoxic functions. In vivo, 89Zr-NK cells trafficked to HER2-postive tumors, with trastuzumab treatment correlating with enhanced 89Zr-NK infiltration. This study demonstrates the feasibility of using PET to image 89Zr-NK cell infiltration into solid tumors.

Abstract 6788: Comparative analysis of anti-HER2 antibody-drug conjugates in tumor spheroid models

Abstract Robust techniques are needed to screen the anti-tumor activity of antibody-drug conjugates (ADCs) in vitro. Traditional monolayer models provide a cost-effective and scalable option but lack many features of the tumor microenvironment, such as complex cell-cell and cell-extracellular matrix interactions. Tumor spheroids afford a more translational model for quantification of ADCs. Here, spheroids are used to compare the mechanism of action (MoA) of anti-HER2 ADCs. Single spheroids were formed from mono- or co-cultures of HER2 over-expressing cancer cells and HER2 negative control cells. Incucyte® Nuclight Green Lentivirus was used to label HER2 positive BT474 cells, whilst HER2 negative MDA-MB-231 cells were left unlabeled. Antibodies were added after 72 hours of spheroid formation in 96-well ultra-low attachment plates. Brightfield and fluorescence images were captured every 3 hours using the Incucyte® Live-Cell Analysis System and quantified, for metrics such as spheroid size and fluorescence intensity, to give an indication of ADC cytotoxicity. Co-culture spheroids were dissociated at assay endpoint and the remaining proportion of green and unlabeled cells was measured using the iQue® Advanced Flow Cytometry Platform. The area of the BT474 mono-culture spheroids decreased in a concentration-dependent manner in the presence of both ADCs tested (trastuzumab emtansine and trastuzumab deruxtecan), indicating increased cytotoxicity. In contrast, only the highest concentration of trastuzumab, which is the monoclonal antibody backbone on which they are based, resulted in a reduction in spheroid size. This displays the increased anti-tumor activity due to the addition of the ADC payloads. In the co-culture model, only the trastuzumab deruxtecan induced a reduction in spheroid area, with the size of the spheroids in the presence of trastuzumab emtansine and trastuzumab remaining comparable to control levels. Analysis of the spheroid composition using flow cytometry showed that for the BT474 cells, again, percentage cell death compared to the IgG control was greater for the two ADCs, at 96.4% and 97.2%, respectively. This death was greater than with trastuzumab, which induced 46.4% cell death. Unlike in the mono-culture assays, a high level of death of the HER2 low expressing cells was also seen in the co-culture in the presence of trastuzumab deruxtecan (94.1% death compared to control). This displays the unique bystander activity of trastuzumab deruxtecan, which has led to its approval as a treatment for both HER2 high and HER2 low breast cancers. These data display how the Incucyte® Live-Cell Analysis System combined with iQue® Advanced Flow Cytometry can be leveraged to provide comprehensive assessment of the in vitro function of ADCs and how it can enable differences in their MoAs to be distinguished in 3D spheroid models. Citation Format: Kirsty McBain, Kalpana Barnes, Nicola Bevan. Comparative analysis of anti-HER2 antibody-drug conjugates in tumor spheroid models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6788.

Abstract 3523: Self-supervised foundation model captures high-dimensional morphology data from single cell brightfield images

Abstract Our knowledge of cancer biology has advanced with the characterization of distinct cell types and cell states within heterogeneous tumor environments. Many methods for imaging and sorting tumor cells require biomarker labels that alter cell characteristics and create a selection bias. The use of label-free single-cell methods should further improve cancer studies involving viable, unperturbed cells for downstream assays such as RNA-Seq, cell culture expansion, and functional assays. To eliminate biomarkers and enable broader assessment of cells, we developed REM-I, a platform that characterizes and sorts unlabeled single cells based on high-dimensional morphology. Cells are captured with brightfield imaging and processed in real-time by self-supervised deep learning models to generate quantitative AI embeddings representative of cell morphology. A significant technical challenge in building REM-I was developing an AI model based on extracted features from cell images without prior knowledge of cell types, cell preparation, or other application-specific knowledge. Accordingly, we developed the Human Foundation Model (HFM), a hybrid architecture that combines self-supervised learning (SSL) and morphometrics (computer vision) to extract 115 dimensional embeddings representing cell morphology from high-resolution REM-I cell images. SSL produces a foundation model with high generalization capacity that enables hypothesis-free sample exploration and efficient generation of application-specific models. Meanwhile, computer vision extracts features that represent measurable and interpretable concepts (e.g., cell size, shape, texture, intensity). The training process for the HFM self-supervised backbone model utilizes the discriminatory power of supervised tasks. Using synthetic cells and three cancer cell lines, we trained and then validated the reproducibility and generalization capabilities of the resulting model. Results show combining deep learning and morphometrics improve interpretability of data and enable rapid characterization and classification of tumor cells with high accuracy. We also report on the Deepcell® Axon data suite, a tool to analyze data and customize reusable workflows. This includes the ability to store and manage data, visualize high dimensional data as low-dimensional projections, and train classifiers to identify and sort cell populations. To enable compatibility with user-preferred downstream analysis pipelines, Axon provides data export options for images, plots, and embeddings. Our approach allows users of all skill levels to access and interpret AI-enabled morphological profiling. Applications of REM-I include hypothesis-free evaluation of heterogeneous tumor samples, label-free cancer cell enrichment, characterization of distinct cell states, and multi-omic integration. Citation Format: Kiran Saini, Senzeyu Zhang, Ryan Carelli, Kevin B. Jacobs, Amy Wong-Thai, Cris Luengo, Vivian Lu, Anastasia Mavropoulos, Andreja Jovic, Jeanette Mei, Thomas Vollbrecht, Stephane C. Boutet, Mahyar Salek, Maddison Masaeli. Self-supervised foundation model captures high-dimensional morphology data from single cell brightfield images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3523.

Abstract 4877: Morphological detection of chemotherapy resistance in cancer cell lines using AI-based analysis

Abstract Chemoresistance can develop during the course of treatment of many cancers, which can limit the utility of standard therapeutics and lead to poor outcomes. The mechanism by which resistance arises is not well understood, and developing models to study resistance can be challenging. Resistance can spontaneously arise in cell lines, however, it is difficult to separate resistant from sensitive cells using traditional biomarkers, hampering their utility for studying mechanisms. Morphology presents another modality to examine resistance, independently of protein markers, and may solve a previously intractable problem of separating resistant from sensitive cells for further study. Here we sought to study the mechanism of chemoresistance in cell lines using multi-parameter morphology in combination with gene expression and other standard molecular approaches. We induced resistance to specific chemotherapeutic drugs in parent cancer cell lines from lung, ovarian, and melanoma cancers. IC-50s of each drug increased by ≥1 order of magnitude in the resistant cells. Whole exome sequencing and bulk RNAseq were performed to examine differentially expressed genes and pathways between the parent and resistant cell lines. Each cell line was analyzed using multi-dimensional morphology using the Deepcell platform, which extracts &amp;gt;100 morphological features from images of unlabeled single cells using artificial intelligence (AI), advanced imaging, and microfluidics. The morphology features represent both Deep Learning (DL)-derived features and human-interpretable morphometric features, such as size and shape. Unique morphotypes were observed differentiating each combination of parent and resistant cell lines, with notable changes in granularity distinguishing them. A subset of images was used to train a random forest classifier to predict the resistant state of each cell image, which performed with up to 85% accuracy on independent cell images. Strikingly, the combination of DL plus morphometric features outperformed the use of morphometric features alone, demonstrating the increased utility of combined analysis. The top gene pathways and top morphological features were examined to infer potential mechanisms underlying chemoresistance. This work demonstrates that morphology alone can be used to distinguish drug resistant vs. parent populations without the use of markers. Morphology analysis can be applied to understanding complex phenotypes, and future platform improvements will enable sorting of these resistant populations to better identify molecular pathways in both cell lines and in primary tumor samples. Citation Format: Andreja Jovic, Manisha Ray, Ryan Carelli, Kiran Saini, Tiffine Pham, Christian Corona, Stephane C. Boutet, Chassidy Johnson, Mahyar Salek, Maddison Masaeli, Matt Barnes, Cyril Y. Ramathal. Morphological detection of chemotherapy resistance in cancer cell lines using AI-based analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4877.

Abstract 6934: Integrating deep learning morphology profiling, cell sorting, and scRNASeq reveals sample diversity and enriched sub-populations

Abstract Advances in single cell technologies have ushered in a new multi-omics era that has provided more granular information on cell types and functions at unprecedented resolution. Morpholomics (the information encoded by cell morphology) has traditionally been used as a readout of cell identity, state, and function; but scalability, interpretation, and sorting based on morphology has remained challenging. The Deepcell® platform, REM-I, performs high-dimensional morphology analysis of unlabeled, single cells using deep-learning on high-resolution bright-field images captured in microfluidic flow to profile and sort target cells in real-time and at scale. Morphology analysis of single cells is achieved by a self-supervised deep learning foundation model, ‘Human Foundation Model’ (HFM), which combines deep learning features with morphometrics. HFM determines cell identities using morphological features associated with distinct processes characterized by granules, vesicles, cell size, pigmentation. and others. To demonstrate the ability of HFM modeling to distinguish and characterize a diversity of cell types representative of tumor microenvironments, we processed various sample types on the REM-I platform. Human melanoma, immune cells, stromal cells, and dissociated biopsies were combined to recapitulate heterogeneous tumor samples. HFM analysis enabled morphology-based exploration of the resulting bright-field images, revealing the presence of various morphologically distinct populations within each sample. Tumor cells were morphologically distinct and clustered separately from non-tumor cells. Immune cells exhibited subtle but separable morphologies, including T cells of distinct activation states. To demonstrate the morphology-based sorting capabilities of REM-I, we applied it to a ‘reference sample’ consisting of several fixed cell lines, at proportions ranging from 1% to &amp;gt;90%. Using only brightfield images of single cells analyzed through HFM, we used the platform to enrich each cell line from the ‘reference sample’ with high yield and purity, and identified cell types constituting a small percentage (~1%) of the total population. We next showed that unlabeled cells enriched on REM-I that are characterized as unperturbed and viable are amenable to downstream molecular analyses. These cell populations were sorted and the retrieved live cells were analyzed by scRNA-Seq (10x Chromium), demonstrating enrichment of various cell types of the tumor microenvironment in each cell population relative to the pre-sorted sample. These results illustrate: 1) the ability to perform downstream molecular analyses on sorted cells from REM-I, 2) the platform can be used to parse the tumor microenvironment, and 3) the link between morphology, cell identity, and molecular signatures. Citation Format: Andreja Jovic, Christian Corona, Kiran Saini, Tiffine Pham, Ryan Carelli, Vivian Lu, Senzeyu Zhang, Stephane C. Boutet, Maddison Masaeli. Integrating deep learning morphology profiling, cell sorting, and scRNASeq reveals sample diversity and enriched sub-populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6934.

The number of nuclei in compacted embryos, assessed by optical coherence microscopy, is a non-invasive and robust marker of mouse embryo quality.

Optical coherence microscopy (OCM) visualizes nuclei in live, unlabeled cells. As most cells are uninucleated, the number of nuclei in embryos may serve as a proxy of the cell number, providing important information on developmental status of the embryo. Importantly, no other non-invasive method currently allows for the cell number count in compacted embryos. We addressed the question of whether OCM, by providing the number of nuclei in compacted mouse embryos, may help evaluate embryo quality. We subjected compacted embryonic Day 3 (E3.0: 72 h after onset of insemination) mouse embryos to OCM scanning and correlated nuclei number and developmental potential. Implantation was assessed using an outgrowth assay (in vitro model meant to reflect embryonic ability to implant in vivo). Embryos with more cells at E3.0 (>18 cells) were more likely to reach the blastocyst stage by E4.0 and E5.0 (P ≪ 0.001) and initiate hatching by E5.0 (P < 0.05) than those with fewer cells (<12 cells). Moreover, the number of cells at E3.0 strongly correlated with the total number of cells in E4.0 and E5.0 embryos (ρ = 0.71, P ≪ 0.001 and ρ = 0.61, P ≪ 0.001, respectively), also when only E4.0 and E5.0 blastocysts were considered (ρ = 0.58, P ≪ 0.001 and ρ = 0.56, P ≪ 0.001, respectively). Additionally, we observed a strong correlation between the number of cells at E3.0 and the number of trophectoderm cells in E4.0 and E5.0 blastocysts (ρ = 0.59, P ≪ 0.001 and ρ = 0.57, P ≪ 0.001, respectively). Importantly, embryos that had more cells at E3.0 (>18 cells) were also more likely to implant in vitro than their counterparts with fewer cells (<12 cells; P ≪ 0.001). Finally, we tested the safety of OCM imaging, demonstrating that OCM scanning affected neither the amount of reactive oxygen species nor mitochondrial activity in the embryos. OCM also did not hinder their preimplantation development, ability to implant in vitro, or to develop to term after transfer to recipient females. Our data indicate that OCM imaging provides important information on embryo quality. As the method seems to be safe for embryos, it could be a valuable addition to the current repertoire of embryo evaluation methods. However, our study was conducted only on mouse embryos, so the proposed protocol would require optimization in order to be applied in other species.

Palladium-103 (103Pd/103mRh), a promising Auger-electron emitter for targeted radionuclide therapy of disseminated tumor cells - absorbed doses in single cells and clusters, with comparison to 177Lu and 161Tb.

Early use of targeted radionuclide therapy (TRT) to eradicate disseminated tumor cells (DTCs) might offer cure. Selection of appropriate radionuclides is required. This work highlights the potential of 103Pd (T1/2 = 16.991 d) which decays to 103mRh (T1/2 = 56.12 min) then to stable 103Rh with emission of Auger and conversion electrons. Methods: The Monte Carlo track structure code CELLDOSE was used to assess absorbed doses in single cells (14-μm diameter; 10-μm nucleus) and clusters of 19 cells. The radionuclide was distributed on the cell surface, within the cytoplasm, or in the nucleus. Absorbed doses from 103Pd, 177Lu and 161Tb were compared after energy normalization. The impact of non-uniform cell targeting, and the potential benefit from dual-targeting was investigated. Additional results related to 103mRh, if used directly, are provided. Results: In the single cell, and depending on radionuclide distribution, 103Pd delivered 7- to 10-fold higher nuclear absorbed dose and 9- to 25-fold higher membrane dose than 177Lu. In the 19-cell clusters, 103Pd absorbed doses also largely exceeded 177Lu. In both situations, 161Tb stood in-between 103Pd and 177Lu. Non-uniform targeting, considering four unlabeled cells within the cluster, resulted in moderate-to-severe dose heterogeneity. For example, with intranuclear 103Pd, unlabeled cells received only 14% of the expected nuclear dose. Targeting with two 103Pd-labeled radiopharmaceuticals minimized dose heterogeneity. Conclusion: 103Pd, a next-generation Auger emitter, can deliver substantially higher absorbed doses than 177Lu to single tumor cells and cell clusters. This may open new horizons for the use of TRT in adjuvant or neoadjuvant settings, or for targeting minimal residual disease.

Optical Fiber-Based Module for Selection and Picking of Cells and Cell Clusters

We have developed an optical fiber-based module that can select, retrieve, and transfer single cells, and cell clusters. Cell picking and isolation has several applications such as separating circulating tumor cells, isolating single fetal cells for prenatal testing, and others. Our Lab-in-a-Fiber (LiF) module can detect fluorescent cancer cells (MCF-7) from a mixture of labeled and unlabeled cells and pick them up for further analysis. The cells picked up by the fiber show a 90% survival rate on viability tests, making this cell-picking technique an attractive alternative to existing methods.

Automated tracking of cell migration in phase contrast images with CellTraxx

The ability of cells to move and migrate is required during development, but also in the adult in processes such as wound healing and immune responses. In addition, cancer cells exploit the cells’ ability to migrate and invade to spread into nearby tissue and eventually metastasize. The majority of cancer deaths are caused by metastasis and the process of cell migration is therefore intensively studied. A common way to study cell migration is to observe cells through an optical microscope and record their movements over time. However, segmenting and tracking moving cells in phase contrast time-lapse video sequences is a challenging task. Several tools to track the velocity of migrating cells have been developed. Unfortunately, most of the automated tools are made for fluorescence images even though unlabelled cells are often preferred to avoid phototoxicity. Consequently, researchers are constrained with laborious manual tracking tools using ImageJ or similar software. We have therefore developed a freely available, user-friendly, automated tracking tool called CellTraxx. This software makes it easy to measure the velocity and directness of migrating cells in phase contrast images. Here, we demonstrate that our tool efficiently recognizes and tracks unlabelled cells of different morphologies and sizes (HeLa, RPE1, MDA-MB-231, HT1080, U2OS, PC-3) in several types of cell migration assays (random migration, wound healing and cells embedded in collagen). We also provide a detailed protocol and download instructions for CellTraxx.

Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion.

Despite continuous technological improvements in sample preparation, mass-spectrometry-based proteomics for trace samples faces the challenges of sensitivity, quantification accuracy, and reproducibility. Herein, we explored the applicability of turboDDA (a method that uses data-dependent acquisition without dynamic exclusion) for quantitative proteomics of trace samples. After systematic optimization of acquisition parameters, we compared the performance of turboDDA with that of data-dependent acquisition with dynamic exclusion (DEDDA). By benchmarking the analysis of trace unlabeled human cell digests, turboDDA showed substantially better sensitivity in comparison with DEDDA, whether for unfractionated or high pH fractionated samples. Furthermore, through designing an iTRAQ-labeled three-proteome model (i.e., tryptic digest of protein lysates from yeast, human, and E. coli) to document the interference effect, we evaluated the quantification interference, accuracy, reproducibility of iTRAQ labeled trace samples, and the impact of PIF (precursor intensity fraction) cutoff for different approaches (turboDDA and DEDDA). The results showed that improved quantification accuracy and reproducibility could be achieved by turboDDA, while a more stringent PIF cutoff resulted in more accurate quantification but less peptide identification for both approaches. Finally, the turboDDA strategy was applied to the differential analysis of limited amounts of human lung cancer cell samples, showing great promise in trace proteomics sample analysis.

COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning

Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without the need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in the ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.

Weakly supervised bilayer convolutional network in segmentation of HER2 related cells to guide HER2 targeted therapies.

Overexpression of human epidermal growth factor receptor 2 (HER2/ERBB2) is identified as a prognostic marker in metastatic breast cancer and a predictor to determine the effects of ERBB2-targeted drugs. Accurate ERBB2 testing is essential in determining the optimal treatment for metastatic breast cancer patients. Brightfield dual in situ hybridization (DISH) was recently authorized by the United States Food and Drug Administration for the assessment of ERRB2 overexpression, which however is a challenging task due to a variety of reasons. Firstly, the presence of touching clustered and overlapping cells render it difficult for segmentation of individual HER2 related cells, which must contain both ERBB2 and CEN17 signals. Secondly, the fuzzy cell boundaries make the localization of each HER2 related cell challenging. Thirdly, variation in the appearance of HER2 related cells is large. Fourthly, as manual annotations are usually made on targets with high confidence, causing sparsely labeled data with some unlabeled HER2 related cells defined as background, this will seriously confuse fully supervised AI learning and cause poor model outcomes. To deal with all issues mentioned above, we propose a two-stage weakly supervised deep learning framework for accurate and robust assessment of ERBB2 overexpression. The effectiveness and robustness of the proposed deep learning framework is evaluated on two DISH datasets acquired at two different magnifications. The experimental results demonstrate that the proposed deep learning framework achieves an accuracy of 96.78 ± 1.25, precision of 97.77 ± 3.09, recall of 84.86 ± 5.83 and Dice Index of 90.77 ± 4.1 and an accuracy of 96.43 ± 2.67, precision of 97.82 ± 3.99, recall of 87.14 ± 10.17 and Dice Index of 91.87 ± 6.51 for segmentation of ERBB2 overexpression on the two experimental datasets, respectively. Furthermore, the proposed deep learning framework outperforms 15 state-of-the-art benchmarked methods by a significant margin (P<0.05) with respect to IoU on both datasets.

Self-supervised deep learning for highly efficient spatial immunophenotyping

Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets. This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model. With 1% annotations (18-114cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828-11,459 annotated cells (-0.002 to-0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P=0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression. By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data. This study was funded by the Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre.

Improved magnetic delivery of cells to the trabecular meshwork in mice

Glaucoma is the leading cause of irreversible blindness worldwide and its most prevalent subtype is primary open angle glaucoma (POAG). One pathological change in POAG is loss of cells in the trabecular meshwork (TM), which is thought to contribute to ocular hypertension and has thus motivated development of cell-based therapies to refunctionalize the TM. TM cell therapy has shown promise in intraocular pressure (IOP) control, but existing cell delivery techniques suffer from poor delivery efficiency. We employed a novel magnetic delivery technique to reduce the unwanted side effects of off-target cell delivery. Mesenchymal stem cells (MSCs) were labeled with superparamagnetic iron oxide nanoparticles (SPIONs) and after intracameral injection were magnetically steered towards the TM using a focused magnetic apparatus (“point magnet”). This technique delivered the cells significantly closer to the TM at higher quantities and with more circumferential uniformity compared to either unlabeled cells or those delivered using a “ring magnet” technique. We conclude that our point magnet cell delivery technique can improve the efficiency of TM cell therapy and in doing so, potentially increase the therapeutic benefits and lower the risk of complications such as tumorigenicity and immunogenicity.

Comparison of tumor growth assessment using GFP fluorescence and DiI labeling in a zebrafish xenograft model

ABSTRACT DiI is a lipophilic fluorescent dye frequently used to label and trace cells in cell cultures and xenograft models. However, DiI can also transfer from labeled to unlabeled cells, including host organism cells, and label dead cells obscuring interpretation of the results. These limitations of DiI labeling in xenograft models have not been thoroughly investigated. Here we labeled green fluorescent protein (GFP)-expressing MDA-MB-231 cells with DiI to directly compare tumor growth assessment in zebrafish xenografts using the DiI labeling and GFP fluorescence. Our results indicate that the DiI based assessment significantly overestimated tumor growth in zebrafish xenograft models compared to the GFP fluorescence based assessment. The imaging of DiI labeled GFP-expressing MDA-MB-231 cell cultures indicated that the DiI labeling of the membrane is uneven. Analysis of the DiI labeled GFP-expressing MDA-MB-231 cell cultures with flow cytometry indicated that the DiI labeling varied over time while the GFP fluorescence remained unchanged, suggesting that the GFP fluorescence is a more reliable signal for monitoring tumor progression than the DiI labeling. Taken together, our results demonstrate limitations of using DiI labeling for xenograft models and emphasize the need for validating the results based on DiI labeling with other orthogonal methods, such as the ones utilizing genetically encoded fluorophores.

Membrane and Nuclear Absorbed Doses from 177Lu and 161Tb in Tumor Clusters: Effect of Cellular Heterogeneity and Potential Benefit of Dual Targeting-A Monte Carlo Study.

Early use of targeted radionuclide therapy to eradicate tumor cell clusters and micrometastases might offer cure. However, there is a need to select appropriate radionuclides and assess the potential impact of heterogeneous targeting. Methods: The Monte Carlo code CELLDOSE was used to assess membrane and nuclear absorbed doses from 177Lu and 161Tb (β--emitter with additional conversion and Auger electrons) in a cluster of 19 cells (14-μm diameter, 10-μm nucleus). The radionuclide distributions considered were cell surface, intracytoplasmic, or intranuclear, with 1,436 MeV released per labeled cell. To model heterogeneous targeting, 4 of the 19 cells were unlabeled, their position being stochastically determined. We simulated situations of single targeting, as well as dual targeting, with the 2 radiopharmaceuticals aiming at different targets. Results: 161Tb delivered 2- to 6-fold higher absorbed doses to cell membranes and 2- to 3-fold higher nuclear doses than 177Lu. When all 19 cells were targeted, membrane and nuclear absorbed doses were dependent mainly on radionuclide location. With cell surface location, membrane absorbed doses were substantially higher than nuclear absorbed doses, both with 177Lu (38-41 vs. 4.7-7.2 Gy) and with 161Tb (237-244 vs. 9.8-15.1 Gy). However, when 4 cells were not targeted by the cell surface radiopharmaceutical, the membranes of these cells received on average only 9.6% of the 177Lu absorbed dose and 2.9% of the 161Tb dose, compared with a cluster with uniform cell targeting, whereas the impact on nuclear absorbed doses was moderate. With an intranuclear radionuclide location, the nuclei of unlabeled cells received only 17% of the 177Lu absorbed dose and 10.8% of the 161Tb dose, compared with situations with uniform targeting. With an intracytoplasmic location, nuclear and membrane absorbed doses to unlabeled cells were one half to one quarter those obtained with uniform targeting, both for 177Lu and for 161Tb. Dual targeting was beneficial in minimizing absorbed dose heterogeneities. Conclusion: To eradicate tumor cell clusters, 161Tb may be a better candidate than 177Lu. Heterogeneous cell targeting can lead to substantial heterogeneities in absorbed doses. Dual targeting was helpful in reducing dose heterogeneity and should be explored in preclinical and clinical studies.

Characterization of chimeric antigen receptor modified T cells expressing scFv-IL-13Rα2 after radiolabeling with 89Zirconium oxine for PET imaging

BackgroundChimeric antigen receptor (CAR) T cell therapy is an exciting cell-based cancer immunotherapy. Unfortunately, CAR-T cell therapy is associated with serious toxicities such as cytokine release syndrome (CRS) and neurotoxicity. The mechanism of these serious adverse events (SAEs) and how homing, distribution and retention of CAR-T cells contribute to toxicities is not fully understood. Enabling in vitro methods to allow meaningful, sensitive in vivo biodistribution studies is needed to better understand CAR-T cell disposition and its relationship to both effectiveness and safety of these products.MethodsTo determine if radiolabelling of CAR-T cells could support positron emission tomography (PET)-based biodistribution studies, we labeled IL-13Rα2 targeting scFv-IL-13Rα2-CAR-T cells (CAR-T cells) with 89Zirconium-oxine (89Zr-oxine) and characterized and compared their product attributes with non-labeled CAR-T cells. The 89Zr-oxine labeling conditions were optimized for incubation time, temperature, and use of serum for labeling. In addition, T cell subtype characterization and product attributes of radiolabeled CAR-T cells were studied to assess their overall quality including cell viability, proliferation, phenotype markers of T-cell activation and exhaustion, cytolytic activity and release of interferon-γ upon co-culture with IL-13Rα2 expressing glioma cells.ResultsWe observed that radiolabeling of CAR-T cells with 89Zr-oxine is quick, efficient, and radioactivity is retained in the cells for at least 8 days with minimal loss. Also, viability of radiolabeled CAR-T cells and subtypes such as CD4 + , CD8 + and scFV-IL-13Rα2 transgene positive T cell population were characterized and found similar to that of unlabeled cells as determined by TUNEL assay, caspase 3/7 enzyme and granzyme B activity assay. Moreover, there were no significant changes in T cell activation (CD24, CD44, CD69 and IFN-γ) or T cell exhaustion (PD-1, LAG-3 and TIM3) markers expression between radiolabeled and unlabeled CAR-T cells. In chemotaxis assays, migratory capability of radiolabeled CAR-T cells to IL-13Rα2Fc was similar to that of non-labeled cells.ConclusionsImportantly, radiolabeling has minimal impact on biological product attributes including potency of CAR-T cells towards IL-13Rα2 positive tumor cells but not IL-13Rα2 negative cells as measured by cytolytic activity and release of IFN-γ. Thus, IL-13Rα2 targeting CAR-T cells radiolabeled with 89Zr-oxine retain critical product attributes and suggest 89Zr-oxine radiolabeling of CAR-T cells may facilitate biodistribution and tissue trafficking studies in vivo using PET.