SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data.

Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has been central in the field, allowing for large screening campaigns leading to a vast number of data-rich images. Currently, to analyze data of this scale, available open-source software ( i.e. , CellProfiler) requires computational resources that are not available to most laboratories worldwide. In addition, the image-embedded cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. Here we introduce SPACe ( S wift P henotypic A nalysis of Ce lls), an open source, Python-based platform for the analysis of single cell image-based morphological profiles produced by CP experiments. SPACe can process a typical dataset approximately ten times faster than CellProfiler on common desktop computers without loss in mechanism of action (MOA) recognition accuracy. It also computes directional distribution-based distances (Earth Mover's Distance - EMD) of morphological features for quality control and hit calling. We highlight several advantages of SPACe analysis on CP assays, including reproducibility across multiple biological replicates, easy applicability to multiple (∼20) cell lines, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We ultimately illustrate the advantages of SPACe in a screening campaign of cell metabolism small molecule inhibitors which we performed in seven cell lines to highlight the importance of testing perturbations across models.

Low dimensional approximation and generalization of multivariate functions on smooth manifolds using deep ReLU neural networks.

The expressive power of deep neural networks is manifested by their remarkable ability to approximate multivariate functions in a way that appears to overcome the curse of dimensionality. This ability is exemplified by their success in solving high-dimensional problems where traditional numerical solvers fail due to their limitations in accurately representing high-dimensional structures. To provide a theoretical framework for explaining this phenomenon, we analyze the approximation of Hölder functions defined on a d-dimensional smooth manifold M embedded in RD, with d≪D, using deep neural networks. We prove that the uniform convergence estimates of the approximation and generalization errors by deep neural networks with ReLU activation functions do not depend on the ambient dimension D of the function but only on its lower manifold dimension d, in a precise sense. Our result improves existing results from the literature where approximation and generalization errors were shown to depend weakly on D.

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery.

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

Compressive Sensing Imaging Spectrometer for UV-Vis Stellar Spectroscopy: Instrumental Concept and Performance Analysis.

Compressive sensing (CS) has been proposed as a disruptive approach to developing a novel class of optical instrumentation used in diverse application domains. Thanks to sparsity as an inherent feature of many natural signals, CS allows for the acquisition of the signal in a very compact way, merging acquisition and compression in a single step and, furthermore, offering the capability of using a limited number of detector elements to obtain a reconstructed image with a larger number of pixels. Although the CS paradigm has already been applied in several application domains, from medical diagnostics to microscopy, studies related to space applications are very limited. In this paper, we present and discuss the instrumental concept, optical design, and performances of a CS imaging spectrometer for ultraviolet-visible (UV-Vis) stellar spectroscopy. The instrument-which is pixel-limited in the entire 300 nm-650 nm spectral range-features spectral sampling that ranges from 2.2 nm@300 nm to 22 nm@650 nm, with a total of 50 samples for each spectrum. For data reconstruction quality, the results showed good performance, measured by several quality metrics chosen from those recommended by CCSDS. The designed instrument can achieve compression ratios of 20 or higher without a significant loss of information. A pros and cons analysis of the CS approach is finally carried out, highlighting main differences with respect to a traditional system.

Automated detection of GFAP-labeled astrocytes in micrographs using YOLOv5.

Astrocytes, a subtype of glial cells with a complex morphological structure, are active players in many aspects of the physiology of the central nervous system (CNS). However, due to their highly involved interaction with other cells in the CNS, made possible by their morphological complexity, the precise mechanisms regulating astrocyte function within the CNS are still poorly understood. This knowledge gap is also due to the current limitations of existing quantitative image analysis tools that are unable to detect and analyze images of astrocyte with sufficient accuracy and efficiency. To address this need, we introduce a new deep learning framework for the automated detection of GFAP-immunolabeled astrocytes in brightfield or fluorescent micrographs. A major novelty of our approach is the applications of YOLOv5, a sophisticated deep learning platform designed for object detection, that we customized to derive optimized classification models for the task of astrocyte detection. Extensive numerical experiments using multiple image datasets show that our method performs very competitively against both conventional and state-of-the-art methods, including the case of images where astrocytes are very dense. In the spirit of reproducible research, our numerical code and annotated data are released open source and freely available to the scientific community.

A Learning Based Framework for Disease Prediction from Images of Human-Derived Pluripotent Stem Cells of Schizophrenia Patients.

Human induced pluripotent stem cells (hiPSCs) have been employed very successfully to identify molecular and cellular features of psychiatric disorders that would be impossible to discover in traditional postmortem studies. Despite the wealth of new available information though, there is still a critical need to establish quantifiable and accessible molecular markers that can be used to reveal the biological causality of the disease. In this paper, we introduce a new quantitative framework based on supervised learning to investigate structural alterations in the neuronal cytoskeleton of hiPSCs of schizophrenia (SCZ) patients. We show that, by using Support Vector Machines or selected Artificial Neural Networks trained on image-based features associated with somas of hiPSCs derived neurons, we can predict very reliably SCZ and healthy control cells. In addition, our method reveals that [Formula: see text]III tubulin and FGF12, two critical components of the cytoskeleton, are differentially regulated in SCZ and healthy control cells, upon perturbation by GSK3 inhibition.

Platelet aggregometry for hip fracture surgery in patients treated with clopidogrel: a pilot study.

Surgery for hip fractures should be performed within 48 h from patient's admission. However, several factors including chronic antiplatelet therapy could delay operation. Among the totality of patients taking clopidogrel, up to 30% are resistant to the drug and have a normal platelets reactivity. We propose thromboelastography (TEG) with an ADP Platelet Mapping assay kit to assess platelet aggregation, a safe tool that could help to avoid surgery delay in those patients treated with antiplatelet therapy. A patient's blood sample was collected for aggregometry. If MA-ADP and platelets aggregation (%) were within normal values, the patient was fit for immediate surgery with neuraxial anesthesia and ultrasound-guided nerve block. If one of the two parameters or both were deranged, a mortality risk assessment was estimated. In the low risk category, the patients waited till normalization of the parameters, whereas in the high-risk group a general anesthesia and peripheral antalgic block was carried out. Nine patients were enrolled. Four of them showed normal aggregometry and surgery was performed within 24 h from admission. Two patients were classified as high mortality risk and surgery was carried out under general anesthesia. Three patients awaited operation till normalization of parameters. No peri or post-operative complications were reported. An aggregometry-guided protocol can safely expedite hip fracture surgery in patients taking clopidogrel. Nonetheless, in presence of a normal platelets function, clinician can opt for a neuraxial instead of general anesthesia reducing the incidence of postoperative delirium and cognitive dysfunction.Trial registration: prospectively registered at clinicaltrials.gov (NCT04642209; date of registration: 23rd November 2020).

Identifying and managing CAR T-cell-mediated toxicities: on behalf of an Italian CAR-T multidisciplinary team.

INTRODUCTION: Chimeric antigen receptor (CAR)-T-cell therapy is a new treatment for patients with hematologic malignancies in which other therapies have failed. AREAS COVERED: The review provides an overview for recognizing and managing the most acute toxicities related to CAR-T cells. EXPERT OPINION: The development of immune-mediated toxicities is a common challenge of CAR-T therapy. The mechanism that determines this toxicity is still unclear, although an unfavorable tumor microenvironment and a pro-inflammatory state put patients at risk. The monitoring, diagnosis, and treatment of post-CAR-T toxicities must be determined and based on international guidelines and internal clinical practice. It is urgent to identify biomarkers that can identify patients at greater risk of developing complications. The adoption of consistent grading criteria is necessary to improve toxicity management strategies continually. The first-line therapy consists of supportive care and treatment with tocilizumab or corticosteroids. An early start of cytokine blockade therapies could mitigate toxicity. The plan will include cytokine release prophylaxis, a risk-adapted treatment, prevention of on-target/off-tumor effect, and a switch on/off CAR-T approach.

Inhibition of AKT Signaling Alters ßIV Spectrin Distribution at the AIS and Increases Neuronal Excitability.

The axon initial segment (AIS) is a highly regulated subcellular domain required for neuronal firing. Changes in the AIS protein composition and distribution are a form of structural plasticity, which powerfully regulates neuronal activity and may underlie several neuropsychiatric and neurodegenerative disorders. Despite its physiological and pathophysiological relevance, the signaling pathways mediating AIS protein distribution are still poorly studied. Here, we used confocal imaging and whole-cell patch clamp electrophysiology in primary hippocampal neurons to study how AIS protein composition and neuronal firing varied in response to selected kinase inhibitors targeting the AKT/GSK3 pathway, which has previously been shown to phosphorylate AIS proteins. Image-based features representing the cellular pattern distribution of the voltage-gated Na+ (Nav) channel, ankyrin G, ßIV spectrin, and the cell-adhesion molecule neurofascin were analyzed, revealing ßIV spectrin as the most sensitive AIS protein to AKT/GSK3 pathway inhibition. Within this pathway, inhibition of AKT by triciribine has the greatest effect on ßIV spectrin localization to the AIS and its subcellular distribution within neurons, a phenotype that Support Vector Machine classification was able to accurately distinguish from control. Treatment with triciribine also resulted in increased excitability in primary hippocampal neurons. Thus, perturbations to signaling mechanisms within the AKT pathway contribute to changes in ßIV spectrin distribution and neuronal firing that may be associated with neuropsychiatric and neurodegenerative disorders.

Efficacy and Effect of Inhaled Adenosine Treatment in Hospitalized COVID-19 Patients.

Lack of specific antiviral treatment for COVID-19 has resulted in long hospitalizations and high mortality rate. By harnessing the regulatory effects of adenosine on inflammatory mediators, we have instituted a new therapeutic treatment with inhaled adenosine in COVID-19 patients, with the aim of reducing inflammation, the onset of cytokine storm, and therefore to improve prognosis. The use of inhaled adenosine in COVID19 patients has allowed reduction of length of stay, on average 6 days. This result is strengthened by the decrease in SARS-CoV-2 positive days. In treated patients compared to control, a clear improvement in PaO2/FiO2 was observed together with a reduction in inflammation parameters, such as the decrease of CRP level. Furthermore, the efficacy of inhaled exogenous adenosine led to an improvement of the prognosis indices, NLR and PLR. The treatment seems to be safe and modulates the immune system, allowing an effective response against the viral infection progression, reducing length of stay and inflammation parameters.

Aggregometry and thromboelastography to identify the timing to trach a COVID-19 patient receiving both antiplatelet therapy and enoxaparin.

In COVID-19 patients receiving enoxaparin and antiplatelets therapy, aggregometry and thromboelastography might be considered an adjunctive tool to identify the time to perform procedures at risk of bleeding, such as tracheostomy.

Case Report: Canakinumab for the Treatment of a Patient With COVID-19 Acute Respiratory Distress Syndrome.

Severe cases of COVID-19 present with serious lung inflammation, acute respiratory distress syndrome and multiorgan damage. SARS-CoV-2 infection is associated with high cytokine levels, including interleukin-6 and certain subsets of immune cells, in particular, NK, distinguished according to the cell surface density of CD56. Cytokine levels are inversely correlated with lymphocyte count, therefore cytokine release syndrome may be an impediment to the adaptive immune response against SARS-CoV-2 infection. Canakinumab, a monoclonal antibody targeting IL-1ß is under investigation for the treatment of severe SAR-CoV-2 infection. An 85 year old male presenting in our hospital with COVID-19, whose condition was complicated by acute respiratory distress syndrome and cardiac and renal failure (with oliguria) after 25 days of hospitalization, was intubated and received canakinumab for compassionate use. On the next day, diuresis recovered and conditions improved: high IL-6 levels and NK cells expressing CD56 bright (associated with cytokine relase) were significantly reduced giving rise to NK CD56 dim . Patient died on day 58 with pulmonary bacterial superinfection and persistent SARS-CoV-2 positivity. In conclusion, canakinumab rescued a high risk, very elderly patient, from multiorgan damage complicating COVID-19. It may represent an useful treatment in severe cases.

Fat mass affects nutritional status of ICU COVID-19 patients.

BACKGROUND: Obesity and steatosis are associated with COVID-19 severe pneumonia. Elevated levels of pro-inflammatory cytokines and reduced immune response are typical of these patients. In particular, adipose tissue is the organ playing the crucial role. So, it is necessary to evaluate fat mass and not simpler body mass index (BMI), because BMI leaves a portion of the obese population unrecognized. The aim is to evaluate the relationship between Percentage of Fat Mass (FM%) and immune-inflammatory response, after 10 days in Intensive Care Unit (ICU). METHODS: Prospective observational study of 22 adult patients, affected by COVID-19 pneumonia and admitted to the ICU and classified in two sets: (10) lean and (12) obese, according to FM% and age (De Lorenzo classification). Patients were analyzed at admission in ICU and at 10th day. RESULTS: Obese have steatosis, impaired hepatic function, compromise immune response and higher inflammation. In addition, they have a reduced prognostic nutritional index (PNI), nutritional survival index for ICU patients. CONCLUSION: This is the first study evaluating FM% in COVID-19 patient. We underlined obese characteristic with likely poorly prognosis and an important misclassification of obesity. A not negligible number of patients with normal BMI could actually have an excess of adipose tissue and therefore have an unfavorable outcome such as an obese. Is fundamental personalized patients nutrition basing on disease phases.

A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissue.

While astrocytes have been traditionally described as passive supportive cells, studies during the last decade have shown they are active players in many aspects of CNS physiology and function both in normal and disease states. However, the precise mechanisms regulating astrocytes function and interactions within the CNS are still poorly understood. This knowledge gap is due in large part to the limitations of current image analysis tools that cannot process astrocyte images efficiently and to the lack of methods capable of quantifying their complex morphological characteristics. To provide an unbiased and accurate framework for the quantitative analysis of fluorescent images of astrocytes, we introduce a new automated image processing pipeline whose main novelties include an innovative module for cell detection based on multiscale directional filters and a segmentation routine that leverages deep learning and sparse representations to reduce the need of training data and improve performance. Extensive numerical tests show that our method performs very competitively with respect to state-of-the-art methods also in challenging images where astrocytes are clustered together. Our code is released open source and freely available to the scientific community.

Imaging of the Axon Initial Segment.

The axon initial segment (AIS) is the first 20- to 60-µm segment of the axon proximal to the soma of a neuron. This highly specialized subcellular domain is the initiation site of the action potential and contains a high concentration of voltage-gated ion channels held in place by a complex nexus of scaffolding and regulatory proteins that ensure proper electrical activity of the neuron. Studies have shown that dysfunction of many AIS channels and scaffolding proteins occurs in a variety of neuropsychiatric and neurodegenerative diseases, raising the need to develop accurate methods for visualization and quantification of the AIS and its protein content in models of normal and disease conditions. In this article, we describe methods for immunolabeling AIS proteins in cultured neurons and brain slices as well as methods for quantifying protein expression and pattern distribution using fluorescent labeling of these proteins. © 2019 by John Wiley & Sons, Inc.

Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer.

Fluorescence confocal microscopy has become increasingly more important in neuroscience due to its applications in image-based screening and profiling of neurons. Multispectral confocal imaging is useful to simultaneously probe for distribution of multiple analytes over networks of neurons. However, current automated image analysis algorithms are not designed to extract single-neuron arbors in images where neurons are not separated, hampering the ability map fluorescence signals at the single cell level. To overcome this limitation, we introduce NeuroTreeTracer - a novel image processing framework aimed at automatically extracting and sorting single-neuron traces in fluorescent images of multicellular neuronal networks. This method applies directional multiscale filters for automated segmentation of neurons and soma detection, and includes a novel tracing routine that sorts neuronal trees in the image by resolving network connectivity even when neurites appear to intersect. By extracting each neuronal tree, NeuroTreetracer enables to automatically quantify the spatial distribution of analytes of interest in the subcellular compartments of individual neurons. This software is released open-source and freely available with the goal to facilitate applications in neuron screening and profiling.

Improved detection of soma location and morphology in fluorescence microscopy images of neurons.

BACKGROUND: Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures. NEW METHOD: In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution. RESULTS: Extensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones. COMPARISON WITH EXISTING METHODS: We include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency. CONCLUSIONS: Our algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community.

Multiscale Analysis of Neurite Orientation and Spatial Organization in Neuronal Images.

The spatial organization of neurites, the thin processes (i.e., dendrites and axons) that stem from a neuron's soma, conveys structural information required for proper brain function. The alignment, direction and overall geometry of neurites in the brain are subject to continuous remodeling in response to healthy and noxious stimuli. In the developing brain, during neurogenesis or in neuroregeneration, these structural changes are indicators of the ability of neurons to establish axon-to-dendrite connections that can ultimately develop into functional synapses. Enabling a proper quantification of this structural remodeling would facilitate the identification of new phenotypic criteria to classify developmental stages and further our understanding of brain function. However, adequate algorithms to accurately and reliably quantify neurite orientation and alignment are still lacking. To fill this gap, we introduce a novel algorithm that relies on multiscale directional filters designed to measure local neurites orientation over multiple scales. This innovative approach allows us to discriminate the physical orientation of neurites from finer scale phenomena associated with local irregularities and noise. Building on this multiscale framework, we also introduce a notion of alignment score that we apply to quantify the degree of spatial organization of neurites in tissue and cultured neurons. Numerical codes were implemented in Python and released open source and freely available to the scientific community.