Automatic Stem Cell Detection in Microscopic Whole Mouse Cryo-Imaging

3D autofluorescence imaging of hydronephrosis and renal anatomical structure using cryo-micro-optical sectioning tomography

Rationale: Mesoscopic visualization of the main anatomical structures of the whole kidney in vivo plays an important role in the pathological diagnosis and exploration of the etiology of hydronephrosis. However, traditional imaging methods cannot achieve whole-kidney imaging with micron resolution under conditions representing in vivo perfusion. Methods: We used in vivo cryofixation (IVCF) to fix acute obstructive hydronephrosis (unilateral ureteral obstruction, UUO), chronic spontaneous hydronephrosis (db/db mice), and their control mouse kidneys for cryo-micro-optical sectioning tomography (cryo-MOST) autofluorescence imaging. We quantitatively assessed the kidney-wide pathological changes in the main anatomical structures, including hydronephrosis, renal subregions, arteries, veins, glomeruli, renal tubules, and peritubular functional capillaries. Results: By comparison with microcomputed tomography imaging, we confirmed that IVCF can maintain the status of the kidney in vivo. Cryo-MOST autofluorescence imaging can display the main renal anatomical structures with a cellular resolution without contrast agents. The hydronephrosis volume reached 26.11 ± 6.00 mm3 and 13.01 ± 3.74 mm3 in 3 days after UUO and in 15-week-old db/db mouse kidneys, respectively. The volume of the cortex and inner stripe of the outer medulla (ISOM) increased while that of the inner medulla (IM) decreased in UUO mouse kidneys. Db/db mice also showed an increase in the volume of the cortex and ISOM volume but no atrophy in the IM. The diameter of the proximal convoluted tubule and proximal straight tubule increased in both UUO and db/db mouse kidneys, indicating that proximal tubules were damaged. However, some renal tubules showed abnormal central bulge highlighting in the UUO mice, but the morphology of renal tubules was normal in the db/db mice, suggesting differences in the pathology and severity of hydronephrosis between the two models. UUO mouse kidneys also showed vascular damage, including segmental artery and vein atrophy and arcuate vein dilation, and the density of peritubular functional capillaries in the cortex and IM was reduced by 37.2% and 49.5%, respectively, suggesting renal hypoxia. In contrast, db/db mouse kidneys showed a normal vascular morphology and peritubular functional capillary density. Finally, we found that the db/db mice displayed vesicoureteral reflux and bladder overactivity, which may be the cause of hydronephrosis formation. Conclusions: We observed and compared main renal structural changes in hydronephrosis under conditions representing in vivo perfusion in UUO, db/db, and control mice through cryo-MOST autofluorescence imaging. The results indicate that cryo-MOST with IVCF can serve as a simple and powerful tool to quantitatively evaluate the in vivo pathological changes in three dimensions, especially the distribution of body fluids in the whole kidney. This method is potentially applicable to the three-dimensional visualization of other tissues, organs, and even the whole body, which may provide new insights into pathological changes in diseases.

Optimal slice thickness for improved accuracy of quantitative analysis of fluorescent cell and microsphere distribution in cryo-images

Cryo-imaging has been effectively used to study the biodistribution of fluorescent cells or microspheres in animal models. Sequential slice-by-slice fluorescent imaging enables detection of fluorescent cells or microspheres for corresponding quantification of their distribution in tissue. However, if slices are too thin, there will be data overload and excessive scan times. If slices are too thick, then cells can be missed. In this study, we developed a model for detection of fluorescent cells or microspheres to aid optimal slice thickness determination. Key factors include: section thickness (X), fluorescent cell intensity (I_fluo), effective tissue attenuation coefficient (μ_T), and a detection threshold (T). The model suggests an optimal slice thickness value that provides near-ideal sensitivity while minimizing scan time. The model also suggests a correction method to compensate for missed cells in the case that image data were acquired with overly large slice thickness. This approach allows cryo-imaging operators to use larger slice thickness to expedite the scan time without significant loss of cell count. We validated the model using real data from two independent studies: fluorescent microspheres in a pig heart and fluorescently labeled stem cells in a mouse model. Results show that slice thickness and detection sensitivity relationships from simulations and real data were well-matched with 99% correlation and 2% root-mean-square (RMS) error. We also discussed the detection characteristics in situations where key assumptions of the model were not met such as fluorescence intensity variation and spatial distribution. Finally, we show that with proper settings, cryo-imaging can provide accurate quantification of the fluorescent cell biodistribution with remarkably high recovery ratios (number of detections/delivery). As cryo-imaging technology has been used in many biological applications, our optimal slice thickness determination and data correction methods can play a crucial role in further advancing its usability and reliability.

Assessment of therapeutic role of mesenchymal stromal cells in mouse models of graft-versus-host disease using cryo-imaging

Insights regarding the biodistribution and homing of mesenchymal stromal cells (MSCs), as well as their interaction with alloreactive T-cells are critical for understanding how MSCs can regulate graft-versus-host disease (GVHD) following allogeneic (allo) bone marrow transplantation (BMT). We developed novel assays based on 3D, microscopic, cryo-imaging of whole-mouse-sized volumes to assess the therapeutic potential of human MSCs using an established mouse GVHD model. Following infusion, we quantitatively tracked fluorescently labeled, donor-derived, T-cells and third party MSCs in BMT recipients using multispectral cryo-imaging. Specific MSC homing sites were identified in the marginal zones in the spleen and the lymph nodes, where we believe MSC immunomodulation takes place. The number of MSCs found in spleen of the allo BMT recipients was about 200% more than that observed in the syngeneic group. To more carefully define the effects MSCs had on T cell activation and expansion, we developed novel T-cell proliferation assays including secondary lymphoid organ (SLO) enlargement and Carboxyfluoescein succinimidyl ester (CFSE) dilution. As anticipated, significant SLO volume enlargement and CFSE dilution was observed in allo but not syn BMT recipients due to rapid proliferation and expansion of labeled T-cells. MSC treatment markedly attenuated CFSE dilution and volume enlargement of SLO. These assays confirm evidence of potent, in vivo, immunomodulatory properties of MSC following allo BMT. Our innovative platform includes novel methods for tracking cells of interest as well as assessing therapeutic function of MSCs during GVHD induction. Our results support the use of MSCs treatment or prevention of GVHD and illuminate the wider adoption of MSCs as a standard medicinal cell therapy.

Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review

Simple Summary

Lately, investigations of artificial intelligence as an assisting tool for analyzing and identifying stem cells have increased. In this systematic scoping review, we aimed to identify and map the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. After an extensive search for the literature following a structured methodology, we included 27 studies in our systematic scoping review that we extracted the relevant data from. Based on the results of the included studies, artificial intelligence has the potential to serve as an assisting tool in stem cell imaging. However, it is still considered relatively new and under maturation. The goal of our review is to guide and help researchers while planning for future investigations.

Abstract

This systematic scoping review aims to map and identify the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. On the ninth of March 2022, data were collected from five electronic databases (PubMed, Medline, Web of Science, Cochrane, and Scopus) and manual citation searching; all data were gathered in Zotero 5.0. A total of 4422 articles were collected after deduplication; only twenty-seven studies were included in this systematic scoping review after a two-phase screening against inclusion criteria by two independent reviewers. The amount of research in this field is significantly increasing over the years. While the current state of artificial intelligence (AI) can tackle a multitude of medical problems, the consensus amongst researchers remains that AI still falls short in multiple ways that investigators should examine, ranging from the quality of images used in training sets and appropriate sample size, as well as the unexpected events that may occur which the algorithm cannot predict.

Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks

Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables biotechnology applications (e.g., stem cells and metastatic cancer). In this report, we compared three methods of organ segmentation: 2D U-Net with 2D-slices and 3D U-Net with either 3D-whole-mouse or 3D-patches. We evaluated the brain, thymus, lung, heart, liver, stomach, spleen, left and right kidney, and bladder. Training with 63 mice, 2D-slices had the best performance, with median Dice scores of > 0.9 and median Hausdorff distances of < 1.2 mm in eightfold cross validation for all organs, except bladder, which is a problem organ due to variable filling and poor contrast. Results were comparable to those for a second analyst on the same data. Regression analyses were performed to fit learning curves, which showed that 2D-slices can succeed with fewer samples. Review and editing of 2D-slices segmentation results reduced human operator time from ~ 2-h to ~ 25-min, with reduced inter-observer variability. As demonstrations, we used organ segmentation to evaluate size changes in liver disease and to quantify the distribution of therapeutic mesenchymal stem cells in organs. With a 48-GB GPU, we determined that extra GPU RAM improved the performance of 3D deep learning because we could train at a higher resolution.

Quantitative analysis of metastatic breast cancer in mice using deep learning on cryo-image data

Cryo-imaging sections and images a whole mouse and provides ~ 120-GBytes of microscopic 3D color anatomy and fluorescence images, making fully manual analysis of metastases an onerous task. A convolutional neural network (CNN)-based metastases segmentation algorithm included three steps: candidate segmentation, candidate classification, and semi-automatic correction of the classification result. The candidate segmentation generated > 5000 candidates in each of the breast cancer-bearing mice. Random forest classifier with multi-scale CNN features and hand-crafted intensity and morphology features achieved 0.8645 ± 0.0858, 0.9738 ± 0.0074, and 0.9709 ± 0.0182 sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic (ROC), with fourfold cross validation. Classification results guided manual correction by an expert with our in-house MATLAB software. Finally, 225, 148, 165, and 344 metastases were identified in the four cancer mice. With CNN-based segmentation, the human intervention time was reduced from > 12 to ~ 2 h. We demonstrated that 4T1 breast cancer metastases spread to the lung, liver, bone, and brain. Assessing the size and distribution of metastases proves the usefulness and robustness of cryo-imaging and our software for evaluating new cancer imaging and therapeutics technologies. Application of the method with only minor modification to a pancreatic metastatic cancer model demonstrated generalizability to other tumor models.

Human multipotent adult progenitor cells effectively reduce graft-vs-host disease while preserving graft-vs-leukemia activity

Graft‐vs‐host disease (GvHD) limits successful outcomes following allogeneic blood and marrow transplantation (allo‐BMT). We examined whether the administration of human, bone marrow‐derived, multipotent adult progenitor cells (MAPCs™) could regulate experimental GvHD. The immunoregulatory capacity of MAPC cells was evaluated in vivo using established murine GvHD models. Injection of MAPC cells on day +1 (D1) and +4 (D4) significantly reduced T‐cell expansion and the numbers of donor‐derived, Tumor Necrosis Factor Alpha (TNFα) and Interferon Gamma (IFNγ)‐producing, CD4+ and CD8+ cells by D10 compared with untreated controls. These findings were associated with reductions in serum levels of TNFα and IFNγ, intestinal and hepatic inflammation and systemic GvHD as measured by survival and clinical score. Biodistribution studies showed that MAPC cells tracked from the lung and to the liver, spleen, and mesenteric nodes within 24 hours after injection. MAPC cells inhibited mouse T‐cell proliferation in vitro and this effect was associated with reduced T‐cell activation and inflammatory cytokine secretion and robust increases in the concentrations of Prostaglandin E2 (PGE2) and Transforming Growth Factor Beta (TGFβ). Indomethacin and E‐prostanoid 2 (EP2) receptor antagonism both reversed while EP2 agonism restored MAPC cell‐mediated in vitro T‐cell suppression, confirming the role for PGE2. Furthermore, cyclo‐oxygenase inhibition following allo‐BMT abrogated the protective effects of MAPC cells. Importantly, MAPC cells had no effect on the generation cytotoxic T lymphocyte activity in vitro, and the administration of MAPC cells in the setting of leukemic challenge resulted in superior leukemia‐free survival. Collectively, these data provide valuable information regarding the biodistribution and regulatory capacity of MAPC cells, which may inform future clinical trial design.

Hyperspectral imaging and spectral unmixing for improving whole-body fluorescence cryo-imaging

Whole-animal fluorescence cryo-imaging is an established technique that enables visualization of the biodistribution of labeled drugs, contrast agents, functional reporters and cells in detail. However, many tissues produce endogenous autofluorescence, which can confound interpretation of the cryo-imaging volumes. We describe a multi-channel, hyperspectral cryo-imaging system that acquires densely-sampled spectra at each pixel in the 3-dimensional stack. This information enables the use of spectral unmixing to isolate the fluorophore-of-interest from autofluorescence and/or other fluorescent reporters. In phantoms and a glioma xenograft model, we show that the approach improves detection limits, increases tumor contrast, and can dramatically alter image interpretation.

Cryo-imaging of Stem Cell Biodistribution in Mouse Model of Graft-Versus-Host-Disease

We demonstrated the use of multispectral cryo-imaging and software to analyze human mesenchymal stromal cells (hMSCs) biodistribution in mouse models of graft-versus-host-disease (GVHD) following allogeneic bone marrow transplantation (BMT). We injected quantum dot labeled MSCs via tail vein to mice receiving BMT and analyzed hMSC biodistribution in major organs (e.g. lung, liver, spleen, kidneys and bone marrow). We compared the biodistribution of hMSCs in mice following allogeneic BMT recipients (with GVHD) to the biodistribution following syngeneic BMT (without GVHD). Cryo-imaging system revealed cellular biodistribution and redistribution patterns in the animal model. We initially found clusters of cells in the lung that eventually dissociated to single cells and redistributed to other organs within 72 h. The in vivo half-life of the exogenous MSCs was about 21 h. We found that the biodistribution of stromal cells was not related to blood flow, rather cells preferentially homed to specific organs. In conclusion, cryo-imaging was suitable for analyzing the cellular biodistribution. It could provide capabilities of visualizing cells anywhere in the mouse model with single cell sensitivity. By characterizing the biodistribution and anatomical specificity of a therapeutic cellular product, we believe that cryo-imaging can play an important role in the advancement of stem and stromal cell therapies and regenerative medicine.

Digital Microscopy, Image Analysis, and Virtual Slide Repository

Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the development of software for digital image analysis, digital slides further pave the way for the development of tools that extract quantitative data from tissue-based studies. This review introduces digital microscopy and pathology, and addresses technical and scientific considerations in slide scanning, quantitative image analysis, and slide repositories. It also highlights the current state of the technology and factors that need to be taken into account to insure optimal utility, including preanalytical considerations and the importance of involving a pathologist in all major steps along the digital microscopy and pathology workflow.

Segmentation and Diagnosis of Liver Carcinoma Based on Adaptive Scale-Kernel Fuzzy Clustering Model for CT Images

Medical image analysis plays an important role in computer-aided liver-carcinoma diagnosis. Aiming at the existing image fuzzy clustering segmentation being not suitable to segment CT image with non-uniform background, a fast robust kernel space fuzzy clustering segmentation algorithm is proposed. Firstly, the sample in euclidean space is mapped into the high dimensional feature space through the kernel function. Then the linear weighted filtering image is obtained by combining the current pixel with its neighborhood pixels through the space information in CT image. Finally, the two-dimensional histogram between the clustered pixel and its neighborhood mean is introduced into the robust kernel space image fuzzy clustering, and the iterative expression of the fast robust fuzzy clustering in kernel space is obtained by using Lagrange multiplier method. The experimental results on four databases show that our proposed method can segment liver tumors from abdominal CT volumes effectively and automatically, and the comprehensive segmentation performance of the proposed method is superior to that of several existing methods.

Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.

Cryo-Imaging and Software Platform for Analysis of Molecular MR Imaging of Micrometastases

We created and evaluated a preclinical, multimodality imaging, and software platform to assess molecular imaging of small metastases. This included experimental methods (e.g., GFP-labeled tumor and high resolution multispectral cryo-imaging), nonrigid image registration, and interactive visualization of imaging agent targeting. We describe technological details earlier applied to GFP-labeled metastatic tumor targeting by molecular MR (CREKA-Gd) and red fluorescent (CREKA-Cy5) imaging agents. Optimized nonrigid cryo-MRI registration enabled nonambiguous association of MR signals to GFP tumors. Interactive visualization of out-of-RAM volumetric image data allowed one to zoom to a GFP-labeled micrometastasis, determine its anatomical location from color cryo-images, and establish the presence/absence of targeted CREKA-Gd and CREKA-Cy5. In a mouse with >160 GFP-labeled tumors, we determined that in the MR images every tumor in the lung >0.3 mm² had visible signal and that some metastases as small as 0.1 mm² were also visible. More tumors were visible in CREKA-Cy5 than in CREKA-Gd MRI. Tape transfer method and nonrigid registration allowed accurate (<11 μm error) registration of whole mouse histology to corresponding cryo-images. Histology showed inflammation and necrotic regions not labeled by imaging agents. This mouse-to-cells multiscale and multimodality platform should uniquely enable more informative and accurate studies of metastatic cancer imaging and therapy.

An objective comparison of cell-tracking algorithms

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.