Assessing cell viability with dynamic optical coherence microscopy.

Assessing cell viability is important in many fields of research. Current optical methods to assess cell viability typically involve fluorescent dyes, which are often less reliable and have poor permeability in primary tissues. Dynamic optical coherence microscopy (dOCM) is an emerging tool that provides label-free contrast reflecting changes in cellular metabolism. In this work, we compare the live contrast obtained from dOCM to viability dyes, and for the first time to our knowledge, demonstrate that dOCM can distinguish live cells from dead cells in murine syngeneic tumors. We further demonstrate a strong correlation between dOCM live contrast and optical redox ratio by metabolic imaging in primary mouse liver tissue. The dOCM technique opens a new avenue to apply label-free imaging to assess the effects of immuno-oncology agents, targeted therapies, chemotherapy, and cell therapies using live tumor tissues.

A deep learning framework for classifying microglia activation state using morphology and intrinsic fluorescence lifetime data.

Microglia are the immune cell in the central nervous system (CNS) and exist in a surveillant state characterized by a ramified form in the healthy brain. In response to brain injury or disease including neurodegenerative diseases, they become activated and change their morphology. Due to known correlation between this activation and neuroinflammation, there is great interest in improved approaches for studying microglial activation in the context of CNS disease mechanisms. One classic approach has utilized Microglia's morphology as one of the key indicators of its activation and correlated with its functional state. More recently microglial activation has been shown to have intrinsic NADH metabolic signatures that are detectable via fluorescence lifetime imaging (FLIM). Despite the promise of morphology and metabolism as key fingerprints of microglial function, they has not been analyzed together due to lack of an appropriate computational framework. Here we present a deep neural network to study the effect of both morphology and FLIM metabolic signatures toward identifying its activation status. Our model is tested on 1, 000+ cells (ground truth generated using LPS treatment) and provides a state-of-the-art framework to identify microglial activation and its role in neurodegenerative diseases.

Joint regression-classification deep learning framework for analyzing fluorescence lifetime images using NADH and FAD.

In this paper, we develop a deep neural network based joint classification-regression approach to identify microglia, a resident central nervous system macrophage, in the brain using fluorescence lifetime imaging microscopy (FLIM) data. Microglia are responsible for several key aspects of brain development and neurodegenerative diseases. Accurate detection of microglia is key to understanding their role and function in the CNS, and has been studied extensively in recent years. In this paper, we propose a joint classification-regression scheme that can incorporate fluorescence lifetime data from two different autofluorescent metabolic co-enzymes, FAD and NADH, in the same model. This approach not only represents the lifetime data more accurately but also provides the classification engine a more diverse data source. Furthermore, the two components of model can be trained jointly which combines the strengths of the regression and classification methods. We demonstrate the efficacy of our method using datasets generated using mouse brain tissue which show that our joint learning model outperforms results on the coenzymes taken independently, providing an efficient way to classify microglia from other cells.

Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer.

Recent research has highlighted the importance of key tumor microenvironment features, notably the collagen-rich extracellular matrix (ECM) in characterizing tumor invasion and progression. This led to great interest from both basic researchers and clinicians, including pathologists, to include collagen fiber evaluation as part of the investigation of cancer development and progression. Fibrillar collagen is the most abundant in the normal extracellular matrix, and was revealed to be upregulated in many cancers. Recent studies suggested an emerging theme across multiple cancer types in which specific collagen fiber organization patterns differ between benign and malignant tissue and also appear to be associated with disease stage, prognosis, treatment response, and other clinical features. There is great potential for developing image-based collagen fiber biomarkers for clinical applications, but its adoption in standard clinical practice is dependent on further translational and clinical evaluations. Here, we offer a comprehensive review of the current literature of fibrillar collagen structure and organization as a candidate cancer biomarker, and new perspectives on the challenges and next steps for researchers and clinicians seeking to exploit this information in biomedical research and clinical workflows.

Prior Hypoxia Exposure Enhances Murine Microglial Inflammatory Gene Expression in vitro Without Concomitant H3K4me3 Enrichment.

Hypoxia (Hx) is a component of multiple disorders, including stroke and sleep-disordered breathing, which often precede or are comorbid with neurodegenerative diseases. However, little is known about how hypoxia affects the ability of microglia, resident CNS macrophages, to respond to subsequent inflammatory challenges that are often present during neurodegenerative processes. We, therefore, tested the hypothesis that hypoxia would enhance or "prime" microglial pro-inflammatory gene expression in response to a later inflammatory challenge without programmatically increasing basal levels of pro-inflammatory cytokine expression. To test this, we pre-exposed immortalized N9 and primary microglia to hypoxia (1% O2) for 16 h and then challenged them with pro-inflammatory lipopolysaccharide (LPS) either immediately or 3-6 days following hypoxic exposure. We used RNA sequencing coupled with chromatin immunoprecipitation sequencing to analyze primed microglial inflammatory gene expression and modifications to histone H3 lysine 4 trimethylation (H3K4me3) at the promoters of primed genes. We found that microglia exhibited enhanced responses to LPS 3 days and 6 days post-hypoxia. Surprisingly, however, the majority of primed genes were not enriched for H3K4me3 acutely following hypoxia exposure. Using the bioinformatics tool MAGICTRICKS and reversible pharmacological inhibition, we found that primed genes required the transcriptional activities of NF-κB. These findings provide evidence that hypoxia pre-exposure could lead to persistent and aberrant inflammatory responses in the context of CNS disorders.

Machine Learning Methods for Fluorescence Lifetime Imaging (FLIM) Based Label-Free Detection of Microglia.

Automated computational analysis techniques utilizing machine learning have been demonstrated to be able to extract more data from different imaging modalities compared to traditional analysis techniques. One new approach is to use machine learning techniques to existing multiphoton imaging modalities to better interpret intrinsically fluorescent cellular signals to characterize different cell types. Fluorescence Lifetime Imaging Microscopy (FLIM) is a high-resolution quantitative imaging tool that can detect metabolic cellular signatures based on the lifetime variations of intrinsically fluorescent metabolic co-factors such as nicotinamide adenine dinucleotide [NAD(P)H]. NAD(P)H lifetime-based discrimination techniques have previously been used to develop metabolic cell signatures for diverse cell types including immune cells such as macrophages. However, FLIM could be even more effective in characterizing cell types if machine learning was used to classify cells by utilizing FLIM parameters for classification. Here, we demonstrate the potential for FLIM-based, label-free NAD(P)H imaging to distinguish different cell types using Artificial Neural Network (ANN)-based machine learning. For our biological use case, we used the challenge of differentiating microglia from other glia cell types in the brain. Microglia are the resident macrophages of the brain and spinal cord and play a critical role in maintaining the neural environment and responding to injury. Microglia are challenging to identify as most fluorescent labeling approaches cross-react with other immune cell types, are often insensitive to activation state, and require the use of multiple specialized antibody labels. Furthermore, the use of these extrinsic antibody labels prevents application in in vivo animal models and possible future clinical adaptations such as neurodegenerative pathologies. With the ANN-based NAD(P)H FLIM analysis approach, we found that microglia in cell culture mixed with other glial cells can be identified with more than 0.9 True Positive Rate (TPR). We also extended our approach to identify microglia in fixed brain tissue with a TPR of 0.79. In both cases the False Discovery Rate was around 30%. This method can be further extended to potentially study and better understand microglia's role in neurodegenerative disease with improved detection accuracy.

Microglia activation visualization via fluorescence lifetime imaging microscopy of intrinsically fluorescent metabolic cofactors.

Significance: A major obstacle to studying resident microglia has been their similarity to infiltrating immune cell types and the lack of unique protein markers for identifying the functional state. Given the role of microglia in all neural diseases and insults, accurate tools for detecting their function beyond morphologic alterations are necessary. Aims: We hypothesized that microglia would have unique metabolic fluxes in reduced nicotinamide adenine dinucleotide (NADH) that would be detectable by relative changes in fluorescence lifetime imaging microscopy (FLIM) parameters, allowing for identification of their activation status. Fluorescence lifetime of NADH has been previously demonstrated to show differences in metabolic fluxes. Approach: Here, we investigate the use of the label-free method of FLIM-based detection of the endogenous metabolic cofactor NADH to identify microglia and characterize their activation status. To test whether microglial activation would also confer a unique NADH lifetime signature, murine primary microglial cultures and adult mice were treated with lipopolysaccharide (LPS). Results: We found that LPS-induced microglia activation correlates with detected changes in NADH lifetime and its free-bound ratio. This indicates that NADH lifetime can be used to monitor microglia activation in a label-free fashion. Moreover, we found that there is an LPS dose-dependent change associated with reactive microglia lifetime fluxes, which is also replicated over time after LPS treatment. Conclusion: We have demonstrated a label-free way of monitoring microglia activation via quantifying lifetime of endogenous metabolic coenzyme NADH. Upon LPS-induced activation, there is a significant change in the fluorescence lifetime following activation. Together, these results indicate that NADH FLIM approaches can be used as a method to characterize microglia activation state, both in vitro and ex vivo.

Spinal protein phosphatase 1 constrains respiratory plasticity after sustained hypoxia.

Plasticity is an important aspect of the neural control of breathing. One well-studied form of respiratory plasticity is phrenic long-term facilitation (pLTF) induced by acute intermittent but not sustained hypoxia. Okadaic acid-sensitive protein phosphatases (PPs) differentially regulate phrenic nerve activity with intermittent vs. sustained hypoxia, at least partially accounting for pLTF pattern sensitivity. However, okadaic acid inhibits multiple serine/threonine phosphatases, and the relevant phosphatase (PP1, PP2A, PP5) for pLTF pattern sensitivity has not been identified. Here, we demonstrate that sustained hypoxia (25 min, 9-10.5% O2) elicits phrenic motor facilitation in rats pretreated with bilateral intrapleural injections of small interfering RNAs (siRNAs; Accell-modified to preferentially transfect neurons, 3.33 µM, 3 days) targeting PP1 mRNA (48 ± 14% change from baseline, n = 6) but not PP2A (14 ± 9% baseline, n = 6) or nontargeting siRNAs (4 ± 10% baseline, n = 7). In time control rats (no hypoxia) treated with siRNAs ( n = 6), no facilitation was evident (-9 ± 9% baseline). siRNAs had no effect on the hypoxic phrenic response. Immunohistochemistry revealed PP1 and PP2A protein in identified phrenic motoneurons. Although PP1 and PP2A siRNAs significantly decreased PP1 and PP2A mRNA in PC12 cell cultures, we were not able to verify "knockdown" in vivo after siRNA treatment. On the other hand, PP1 and PP2A siRNAs significantly decreased PP1 and PP2A mRNA in PC12 cell cultures, verifying the intended siRNA effects. In conclusion, PP1 (not PP2A) is the relevant okadaic acid-sensitive phosphatase constraining phrenic motor facilitation after sustained hypoxia and likely contributing to pLTF pattern sensitivity. NEW & NOTEWORTHY This study demonstrates that the relevant okadaic acid-sensitive Ser/Thr protein phosphatase (PP) constraining facilitation after sustained hypoxia is PP1 and not PP2A. It suggests that PP1 may be critical in the pattern sensitivity of hypoxia-induced phrenic motor plasticity.

The exosome complex establishes a barricade to erythroid maturation.

Complex genetic networks control hematopoietic stem cell differentiation into progenitors that give rise to billions of erythrocytes daily. Previously, we described a role for the master regulator of erythropoiesis, GATA-1, in inducing genes encoding components of the autophagy machinery. In this context, the Forkhead transcription factor, Foxo3, amplified GATA-1-mediated transcriptional activation. To determine the scope of the GATA-1/Foxo3 cooperativity, and to develop functional insights, we analyzed the GATA-1/Foxo3-dependent transcriptome in erythroid cells. GATA-1/Foxo3 repressed expression of Exosc8, a pivotal component of the exosome complex, which mediates RNA surveillance and epigenetic regulation. Strikingly, downregulating Exosc8, or additional exosome complex components, in primary erythroid precursor cells induced erythroid cell maturation. Our results demonstrate a new mode of controlling erythropoiesis in which multiple components of the exosome complex are endogenous suppressors of the erythroid developmental program.