Microvascular Network Remodeling in the Ischemic Mouse Brain Defined by Light Sheet Microscopy.

BACKGROUND: Until now, the analysis of microvascular networks in the reperfused ischemic brain has been limited due to tissue transparency challenges. METHODS: Using light sheet microscopy, we assessed microvascular network remodeling in the striatum from 3 hours to 56 days post-ischemia in 2 mouse models of transient middle cerebral artery occlusion lasting 20 or 40 minutes, resulting in mild ischemic brain injury or brain infarction, respectively. We also examined the effect of a clinically applicable S1P (sphingosine-1-phosphate) analog, FTY720 (fingolimod), on microvascular network remodeling. RESULTS: Over 56 days, we observed progressive microvascular degeneration in the reperfused striatum, that is, the lesion core, which was followed by robust angiogenesis after mild ischemic injury induced by 20-minute middle cerebral artery occlusion. However, more severe ischemic injury elicited by 40-minute middle cerebral artery occlusion resulted in incomplete microvascular remodeling. In both cases, microvascular networks did not return to their preischemic state but displayed a chronically altered pattern characterized by higher branching point density, shorter branches, higher unconnected branch density, and lower tortuosity, indicating enhanced network connectivity. FTY720 effectively increased microvascular length density, branching point density, and volume density in both models, indicating an angiogenic effect of this drug. CONCLUSIONS: Utilizing light sheet microscopy together with automated image analysis, we characterized microvascular remodeling in the ischemic lesion core in unprecedented detail. This technology will significantly advance our understanding of microvascular restorative processes and pave the way for novel treatment developments in the stroke field.

Data-driven subtyping of Parkinson's disease: comparison of current methodologies and application to the Bochum PNS cohort.

Considerable efforts have been made to better describe and identify Parkinson's disease (PD) subtypes. Cluster analyses have been proposed as an unbiased development approach for PD subtypes that could facilitate their identification, tracking of progression, and evaluation of therapeutic responses. A data-driven clustering analysis was applied to a PD cohort of 114 subjects enrolled at St. Josef-Hospital of the Ruhr University in Bochum (Germany). A wide spectrum of motor and non-motor scores including polyneuropathy-related measures was included into the analysis. K-means and hierarchical agglomerative clustering were performed to identify PD subtypes. Silhouette and Calinski-Harabasz Score Elbow were then employed as supporting evaluation metrics for determining the optimal number of clusters. Principal Component Analysis (PCA), analysis of variance (ANOVA), and analysis of covariance (ANCOVA) were conducted to determine the relevance of each score for the clusters' definition. Three PD cluster subtypes were identified: early onset mild type, intermediate type, and late-onset severe type. The between-cluster analysis consistently showed highly significant differences (P < 0.01), except for one of the scores measuring polyneuropathy (Neuropathy Disability Score; P = 0.609) and Levodopa dosage (P = 0.226). Parkinson's Disease Questionnaire (PDQ-39), Non-motor Symptom Questionnaire (NMSQuest), and the MDS-UPDRS Part II were found to be crucial factors for PD subtype differentiation. The present analysis identifies a specific set of criteria for PD subtyping based on an extensive panel of clinical and paraclinical scores. This analysis provides a foundation for further development of PD subtyping, including k-means and hierarchical agglomerative clustering.Trial registration: DRKS00020752, February 7, 2020, retrospectively registered.

Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators.

It is well-established that neural networks can predict or identify structural motifs of non-coding RNAs (ncRNAs). Yet, the neural network based identification of RNA structural motifs is limited by the availability of training data that are often insufficient for learning features of specific ncRNA families or structural motifs. Aiming to reliably identify intrinsic transcription terminators in bacteria, we introduce a novel pre-training approach that uses inverse folding to generate training data for predicting or identifying a specific family or structural motif of ncRNA. We assess the ability of neural networks to identify secondary structure by systematic in silico mutagenesis experiments. In a study to identify intrinsic transcription terminators as functionally well-understood RNA structural motifs, our inverse folding based pre-training approach significantly boosts the performance of neural network topologies, which outperform previous approaches to identify intrinsic transcription terminators. Inverse-folding based pre-training provides a simple, yet highly effective way to integrate the well-established thermodynamic energy model into deep neural networks for identifying ncRNA families or motifs. The pre-training technique is broadly applicable to a range of network topologies as well as different types of ncRNA families and motifs.

Dimensionality reduction for deep learning in infrared microscopy: a comparative computational survey.

While infrared microscopy provides molecular information at spatial resolution in a label-free manner, exploiting both spatial and molecular information for classifying the disease status of tissue samples constitutes a major challenge. One strategy to mitigate this problem is to embed high-dimensional pixel spectra in lower dimensions, aiming to preserve molecular information in a more compact manner, which reduces the amount of data and promises to make subsequent disease classification more accessible for machine learning procedures. In this study, we compare several dimensionality reduction approaches and their effect on identifying cancer in the context of a colon carcinoma study. We observe surprisingly small differences between convolutional neural networks trained on dimensionality reduced spectra compared to utilizing full spectra, indicating a clear tendency of the convolutional networks to focus on spatial rather than spectral information for classifying disease status.

Reliable identification and quantification of neural cells in microscopic images of neurospheres.

Neurosphere cultures consisting of primary human neural stem/progenitor cells (hNPC) are used for studying the effects of substances on early neurodevelopmental processes in vitro. Differentiating hNPCs migrate and differentiate into radial glia, neurons, astrocytes, and oligodendrocytes upon plating on a suitable extracellular matrix and thus model processes of early neural development. In order to characterize alterations in hNPC development, it is thus an essential task to reliably identify the cell type of each migrated cell in the migration area of a neurosphere. To this end, we introduce and validate a deep learning approach for identifying and quantifying cell types in microscopic images of differentiated hNPC. As we demonstrate, our approach performs with high accuracy and is robust against typical potential confounders. We demonstrate that our deep learning approach reproduces the dose responses of well-established developmental neurotoxic compounds and controls, indicating its potential in medium or high throughput in vitro screening studies. Hence, our approach can be used for studying compound effects on neural differentiation processes in an automated and unbiased process.

Lead-seq: transcriptome-wide structure probing in vivo using lead(II) ions.

The dynamic conformation of RNA molecules within living cells is key to their function. Recent advances in probing the RNA structurome in vivo, including the use of SHAPE (Selective 2'-Hydroxyl Acylation analyzed by Primer Extension) or kethoxal reagents or DMS (dimethyl sulfate), provided unprecedented insights into the architecture of RNA molecules in the living cell. Here, we report the establishment of lead probing in a global RNA structuromics approach. In order to elucidate the transcriptome-wide RNA landscape in the enteric pathogen Yersinia pseudotuberculosis, we combined lead(II) acetate-mediated cleavage of single-stranded RNA regions with high-throughput sequencing. This new approach, termed 'Lead-seq', provides structural information independent of base identity. We show that the method recapitulates secondary structures of tRNAs, RNase P RNA, tmRNA, 16S rRNA and the rpsT 5'-untranslated region, and that it reveals global structural features of mRNAs. The application of Lead-seq to Y. pseudotuberculosis cells grown at two different temperatures unveiled the first temperature-responsive in vivo RNA structurome of a bacterial pathogen. The translation of candidate genes derived from this approach was confirmed to be temperature regulated. Overall, this study establishes Lead-seq as complementary approach to interrogate intracellular RNA structures on a global scale.

Deep representation learning for domain adaptable classification of infrared spectral imaging data.

MOTIVATION: Applying infrared microscopy in the context of tissue diagnostics heavily relies on computationally preprocessing the infrared pixel spectra that constitute an infrared microscopic image. Existing approaches involve physical models, which are non-linear in nature and lead to classifiers that do not generalize well, e.g. across different types of tissue preparation. Furthermore, existing preprocessing approaches involve iterative procedures that are computationally demanding, so that computation time required for preprocessing does not keep pace with recent progress in infrared microscopes which can capture whole-slide images within minutes. RESULTS: We investigate the application of stacked contractive autoencoders as an unsupervised approach to preprocess infrared microscopic pixel spectra, followed by supervised fine-tuning to obtain neural networks that can reliably resolve tissue structure. To validate the robustness of the resulting classifier, we demonstrate that a network trained on embedded tissue can be transferred to classify fresh frozen tissue. The features obtained from unsupervised pretraining thus generalize across the large spectral differences between embedded and fresh frozen tissue, where under previous approaches separate classifiers had to be trained from scratch. AVAILABILITY AND IMPLEMENTATION: Our implementation can be downloaded from https://github.com/arnrau/SCAE_IR_Spectral_Imaging. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fast and Noninvasive Diagnosis of Cervical Cancer by Coherent Anti-Stokes Raman Scattering.

Cervical cancer is the fourth most common cancer in women worldwide, and early detection of its precancerous lesions can decrease mortality. Cytopathology, HPV testing, and histopathology are the most commonly used tools in clinical practice. However, these methods suffer from many limitations such as subjectivity, cost, and time. Therefore, there is an unmet clinical need to develop new noninvasive methods for the early detection of cervical cancer. Here, a novel noninvasive, fast, and label-free approach with high accuracy is presented using liquid-based cytology Pap smears. CARS and SHG/TPF imaging was performed at one wavenumber on the Pap smears from patients with specimens negative for intraepithelial lesions or malignancy (NILM), and low-grade (LSIL) and high-grade (HSIL) squamous intraepithelial lesions. The normal, LSIL, and HSIL cells were selected on the basis of the ratio of the nucleus to the cytoplasm and cell morphology. Raman spectral imaging of single cells from the same smears was also performed to provide integral biochemical information of cells. Deep convolutional neural networks (DCNNs) were trained independently with CARS, SHG/TPF, and Raman images, taking into account both morphotextural and spectral information. DCNNs based on CARS, SHG/TPF, or Raman images have discriminated between normal and cancerous Pap smears with 100% accuracy. These results demonstrate that CARS/SHG/TPF microscopy has a prospective use as a label-free imaging technique for the fast screening of a large number of cells in cytopathological samples.

Noninvasive Diagnosis of High-Grade Urothelial Carcinoma in Urine by Raman Spectral Imaging.

The current gold standard for the diagnosis of bladder cancer is cystoscopy, which is invasive and painful for patients. Therefore, noninvasive urine cytology is usually used in the clinic as an adjunct to cystoscopy; however, it suffers from low sensitivity. Here, a novel noninvasive, label-free approach with high sensitivity for use with urine is presented. Coherent anti-Stokes Raman scattering imaging of urine sediments was used in the first step for fast preselection of urothelial cells, where high-grade urothelial cancer cells are characterized by a large nucleus-to-cytoplasm ratio. In the second step, Raman spectral imaging of urothelial cells was performed. A supervised classifier was implemented to automatically differentiate normal and cancerous urothelial cells with 100% accuracy. In addition, the Raman spectra not only indicated the morphological changes that are identified by cytology with hematoxylin and eosin staining but also provided molecular resolution through the use of specific marker bands. The respective Raman marker bands directly show a decrease in the level of glycogen and an increase in the levels of fatty acids in cancer cells as compared to controls. These results pave the way for "spectral" cytology of urine using Raman microspectroscopy.

Omnisphero: a high-content image analysis (HCA) approach for phenotypic developmental neurotoxicity (DNT) screenings of organoid neurosphere cultures in vitro.

Current developmental neurotoxicity (DNT) testing in animals faces major limitations, such as high cost and time demands as well as uncertainties in their methodology, evaluation and regulation. Therefore, the use of human-based 3D in vitro systems in combination with high-content image analysis (HCA) might contribute to DNT testing with lower costs, increased throughput and enhanced predictivity for human hazard identification. Human neural progenitor cells (hNPCs) grown as 3D neurospheres mimic basic processes of brain development including hNPC migration and differentiation and are therefore useful for DNT hazard identification. HCA of migrated neurospheres creates new challenges for automated evaluations because it encompasses variable cell densities, inconsistent z-layers and heterogeneous cell populations. We tackle those challenges with our Omnisphero software, which assesses multiple endpoints of the 'Neurosphere Assay.' For neuronal identification, Omnisphero reaches a true positive rate (TPR) of 83.8 % and a false discovery rate (FDR) of 11.4 %, thus being comparable to the interindividual difference among two researchers (TPR = 94.3, FDR = 11.0 %) and largely improving the results obtained by an existing HCA approach, whose TPR does not exceed 50 % at a FDR above 50 %. The high FDR of existing methods results in incorrect measurements of neuronal morphological features accompanied by an overestimation of compound effects. Omnisphero additionally includes novel algorithms to assess 'neurosphere-specific' endpoints like radial migration and neuronal density distribution within the migration area. Furthermore, a user-assisted parameter optimization procedure makes Omnisphero accessible to non-expert end users.

Fully automated registration of vibrational microspectroscopic images in histologically stained tissue sections.

BACKGROUND: In recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools. RESULTS: We propose a corresponding registration algorithm that addresses this registration problem, and demonstrate the robustness of our approach in different constellations of microscopes. First, we deal with subregion registration of Fourier Transform Infrared (FTIR) microscopic images in whole-slide histopathological staining images. Second, we register FTIR imaged cores of tissue microarrays in their histopathologically stained counterparts, and finally perform registration of Coherent anti-Stokes Raman spectroscopic (CARS) images within histopathological staining images. CONCLUSIONS: Our validation involves a large variety of samples obtained from colon, bladder, and lung tissue on three different types of microscopes, and demonstrates that our proposed method works fully automated and highly robust in different constellations of microscopes involving diverse types of tissue samples.

Colocalization of fluorescence and Raman microscopic images for the identification of subcellular compartments: a validation study.

A major promise of Raman microscopy is the label-free detailed recognition of cellular and subcellular structures. To this end, identifying colocalization patterns between Raman spectral images and fluorescence microscopic images is a key step to annotate subcellular components in Raman spectroscopic images. While existing approaches to resolve subcellular structures are based on fluorescence labeling, we propose a combination of a colocalization scheme with subsequent training of a supervised classifier that allows label-free resolution of cellular compartments. Our colocalization scheme unveils statistically significant overlapping regions by identifying correlation between the fluorescence color channels and clusters from unsupervised machine learning methods like hierarchical cluster analysis. The colocalization scheme is used as a pre-selection to gather appropriate spectra as training data. These spectra are used in the second part as training data to establish a supervised random forest classifier to automatically identify lipid droplets and nucleus. We validate our approach by examining Raman spectral images overlaid with fluorescence labelings of different cellular compartments, indicating that specific components may indeed be identified label-free in the spectral image. A Matlab implementation of our colocalization software is available at .

The common ancestral core of vertebrate and fungal telomerase RNAs.

Telomerase is a ribonucleoprotein with an intrinsic telomerase RNA (TER) component. Within yeasts, TER is remarkably large and presents little similarity in secondary structure to vertebrate or ciliate TERs. To better understand the evolution of fungal telomerase, we identified 74 TERs from Pezizomycotina and Taphrinomycotina subphyla, sister clades to budding yeasts. We initially identified TER from Neurospora crassa using a novel deep-sequencing-based approach, and homologous TER sequences from available fungal genome databases by computational searches. Remarkably, TERs from these non-yeast fungi have many attributes in common with vertebrate TERs. Comparative phylogenetic analysis of highly conserved regions within Pezizomycotina TERs revealed two core domains nearly identical in secondary structure to the pseudoknot and CR4/5 within vertebrate TERs. We then analyzed N. crassa and Schizosaccharomyces pombe telomerase reconstituted in vitro, and showed that the two RNA core domains in both systems can reconstitute activity in trans as two separate RNA fragments. Furthermore, the primer-extension pulse-chase analysis affirmed that the reconstituted N. crassa telomerase synthesizes TTAGGG repeats with high processivity, a common attribute of vertebrate telomerase. Overall, this study reveals the common ancestral cores of vertebrate and fungal TERs, and provides insights into the molecular evolution of fungal TER structure and function.

Automated identification of subcellular organelles by coherent anti-stokes Raman scattering.

Coherent anti-Stokes Raman scattering (CARS) is an emerging tool for label-free characterization of living cells. Here, unsupervised multivariate analysis of CARS datasets was used to visualize the subcellular compartments. In addition, a supervised learning algorithm based on the "random forest" ensemble learning method as a classifier, was trained with CARS spectra using immunofluorescence images as a reference. The supervised classifier was then used, to our knowledge for the first time, to automatically identify lipid droplets, nucleus, nucleoli, and endoplasmic reticulum in datasets that are not used for training. These four subcellular components were simultaneously and label-free monitored instead of using several fluorescent labels. These results open new avenues for label-free time-resolved investigation of subcellular components in different cells, especially cancer cells.

Label-free imaging of drug distribution and metabolism in colon cancer cells by Raman microscopy.

Targeted cancer therapies block cancer growth and spread using small molecules. Many molecular targets for an epidermal growth factor receptor (EGFR) selectively compete with the adenosine triphosphate-binding site of its tyrosine kinase domain. Detection of molecular targeted agents and their metabolites in cells/tissues by label-free imaging is attractive because dyes or fluorescent labels may be toxic or invasive. Here, label-free Raman microscopy is applied to show the spatial distribution of the molecular targeted drug erlotinib within the cell. The Raman images show that the drug is clustered at the EGFR protein at the membrane and induces receptor internalization. The changes within the Raman spectrum of erlotinib measured in cells as compared to the free-erlotinib spectrum indicate that erlotinib is metabolized within cells to its demethylated derivative. This study provides detailed insights into the drug targeting mechanism at the atomic level in cells. It demonstrates that Raman microscopy will open avenues as a non-invasive and label-free technique to investigate pharmacokinetics at the highest possible resolution in living cells.

Similarity maps and hierarchical clustering for annotating FT-IR spectral images.

BACKGROUND: Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization. RESULTS: We introduce so-called interactive similarity maps as an alternative annotation strategy for annotating infrared microscopic images. We demonstrate that segmentations obtained from interactive similarity maps lead to similarly accurate segmentations as segmentations obtained from conventionally used hierarchical clustering approaches. In order to perform this comparison on quantitative grounds, we provide a scheme that allows to identify non-horizontal cuts in dendrograms. This yields a validation scheme for hierarchical clustering approaches commonly used in infrared microscopy. CONCLUSIONS: We demonstrate that interactive similarity maps may identify more accurate segmentations than hierarchical clustering based approaches, and thus are a viable and due to their interactive nature attractive alternative to hierarchical clustering. Our validation scheme furthermore shows that performance of hierarchical two-means is comparable to the traditionally used Ward's clustering. As the former is much more efficient in time and memory, our results suggest another less resource demanding alternative for annotating large spectral images.

Spectral histopathology of colon cancer tissue sections by Raman imaging with 532 nm excitation provides label free annotation of lymphocytes, erythrocytes and proliferating nuclei of cancer cells.

Spectral histopathology (SHP) is an emerging tool for label free annotation of tissue. While FTIR based SHP provides fast annotation of larger tissue sections, Raman based SHP is slower but achieves a 10 times higher spatial resolution as compared to FTIR. Usually NIR excitation is used for Raman measurements on biological samples. Here, for the first time 532 nm excitation is used to annotate colon tissue by Raman SHP. Excellent data quality is obtained, which resolves for example erythrocytes and lymphocytes. In addition to Raman scattering auto-fluorescence is observed. We found that this auto-fluorescence overlaps spatially with the fluorescence of antibodies against p53 used in routine immunohistochemistry in surgical pathology. This fluorescence indicates nuclei of cancer cells with mutated p53 and allows new label free assignment of cancer cells. These results open new avenues for optical diagnosis by Raman spectroscopy and autofluorescence.

An image skeletonization-based tool for pollen tube morphology analysis and phenotyping.

The mechanism underlying pollen tube growth involves diverse genes and molecular pathways. Alterations in the regulatory genes or pathways cause phenotypic changes reflected by cellular morphology, which can be captured using fluorescence microscopy. Determining and classifying pollen tube morphological phenotypes in such microscopic images is key to our understanding the involvement of genes and pathways. In this context, we propose a computational method to extract quantitative morphological features, and demonstrate that these features reflect morphological differences relevant to distinguish different defects of pollen tube growth. The corresponding software tool furthermore includes a novel semi-automated image segmentation approach, allowing to highly accurately identify the boundary of a pollen tube in a microscopic image.

Fast and label-free automated detection of microsatellite status in early colon cancer using artificial intelligence integrated infrared imaging.

PURPOSE: Microsatellite instability (MSI) due to mismatch repair (MMR) defects accounts for 15-20% of colon cancers (CC). MSI testing is currently standard of care in CC with immunohistochemistry of the four MMR proteins representing the gold standard. Instead, label-free quantum cascade laser (QCL) based infrared (IR) imaging combined with artificial intelligence (AI) may classify MSI/microsatellite stability (MSS) in unstained tissue sections user-independently and tissue preserving. METHODS: Paraffin-embedded unstained tissue sections of early CC from patients participating in the multicentre AIO ColoPredict Plus (CPP) 2.0 registry were analysed after dividing into three groups (training, test, and validation). IR images of tissue sections using QCL-IR microscopes were classified by AI (convolutional neural networks [CNN]) using a two-step approach. The first CNN (modified U-Net) detected areas of cancer while the second CNN (VGG-Net) classified MSI/MSS. End-points were area under receiver operating characteristic (AUROC) and area under precision recall curve (AUPRC). RESULTS: The cancer detection in the first step was based on 629 patients (train n = 273, test n = 138, and validation n = 218). Resulting classification AUROC was 1.0 for the validation dataset. The second step classifying MSI/MSS was performed on 547 patients (train n = 331, test n = 69, and validation n = 147) reaching AUROC and AUPRC of 0.9 and 0.74, respectively, for the validation cohort. CONCLUSION: Our novel label-free digital pathology approach accurately and rapidly classifies MSI vs. MSS. The tissue sections analysed were not processed leaving the sample unmodified for subsequent analyses. Our approach demonstrates an AI-based decision support tool potentially driving improved patient stratification and precision oncology in the future.

The impact of biostatistics on hazard characterization using in vitro developmental neurotoxicity assays.

In chemical safety assessment, benchmark concentrations (BMC) and their associated uncertainty are needed for the toxicological evaluation of in vitro data sets. A BMC estimation is derived from concentration-response modelling and results from various statistical decisions, which depend on factors such as experimental design and assay endpoint features. In current data practice, the experimenter is often responsible for the data analysis and therefore relies on statistical software, often without being aware of the software default settings and how they can impact the outputs of data analysis. To provide more insight into how statistical decision-making can influence the outcomes of data analysis and interpretation, we have developed an automated platform that includes statistical methods for BMC estimation, a novel endpoint-specific hazard classification system, and routines that flag data sets that are outside the applicability domain for an automatic data evaluation. We used case studies on a large dataset produced by a developmental neurotoxicity (DNT) in vitro battery (DNT IVB). Here we focused on the BMC and its confidence interval (CI) estimation as well as on final hazard classification. We identified five crucial statistical decisions the experimenter must make during data analysis: choice of replicate averaging, response data normalization, regression modelling, BMC and CI estimation, and choice of benchmark response levels. The insights gained are intended to raise more awareness among experimenters on the importance of statistical decisions and methods but also to demonstrate how important fit-for-purpose, internationally harmonized and accepted data evaluation and analysis procedures are for objective hazard classification.