Through statistical analysis of datasets describing single cell shape following systematic gene depletion, we have found that the morphological landscapes explored by cells are composed of a small ...number of attractor states. We propose that the topology of these landscapes is in large part determined by cell‐intrinsic factors, such as biophysical constraints on cytoskeletal organization, and reflects different stable signaling and/or transcriptional states. Cell‐extrinsic factors act to determine how cells explore these landscapes, and the topology of the landscapes themselves. Informational stimuli primarily drive transitions between stable states by engaging signaling networks, while mechanical stimuli tune, or even radically alter, the topology of these landscapes. As environments fluctuate, the topology of morphological landscapes explored by cells dynamically adapts to these fluctuations. Finally we hypothesize how complex cellular and tissue morphologies can be generated from a limited number of simple cell shapes.
As the major neurodegenerative disease of dementia, Alzheimer’s disease (AD) has caused an enormous social and economic burden on society. Currently, AD has neither clear pathogenesis nor effective ...treatments. Positron emission tomography (PET) and magnetic resonance imaging (MRI) have been verified as potential tools for diagnosing and monitoring Alzheimer’s disease. However, the high costs, low spatial resolution, and long acquisition time limit their broad clinical utilization. The gold standard of AD diagnosis routinely used in research is imaging AD biomarkers with dyes or other reagents, which are unsuitable for
in vivo
studies owing to their potential toxicity and prolonged and costly process of the U.S. Food and Drug Administration (FDA) approval for human use. Furthermore, these exogenous reagents might bring unwarranted interference to mechanistic studies, causing unreliable results. Several label-free optical imaging techniques, such as infrared spectroscopic imaging (IRSI), Raman spectroscopic imaging (RSI), optical coherence tomography (OCT), autofluorescence imaging (AFI), optical harmonic generation imaging (OHGI), etc., have been developed to circumvent this issue and made it possible to offer an accurate and detailed analysis of AD biomarkers. In this review, we present the emerging label-free optical imaging techniques and their applications in AD, along with their potential and challenges in AD diagnosis.
Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population ...of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.
Neural progenitor (NP) cells are the multipotent cells that produce neurons and glia in the central nervous system. Compounds regulating their proliferation are key to both understanding brain ...development and unlocking their potential in regenerative repair. We discuss a chemical screen that unexpectedly identified inhibitors of Erk signaling potently promoting the self-renewing divisions of fetal NP cells. This occurred through crosstalk between Erk and Akt signaling cascades. The crosstalk mechanism is cell type-specific, and is not detected in adult NP cells as well as brain tumor cells. The mechanism was also shown to be independent from the GSK-3 signaling pathway, which has been reported to be a major regulator of NP cell homeostasis and inhibitors to which were also identified in the screen. In vitro Erk inhibition led to the prolonged rapid expansion of fetal NP cells while retaining their multipotency. In vivo inhibitor administration significantly inhibited the neuronal differentiation, and resulted in increased proliferative progenitor cells in the ventricular/subventricular zone (VZ/SVZ) of the embryonic cortex. Our results uncovered a novel regulating pathway for NP cell proliferation in the developing brain. The discovery provides a pharmacological basis for in vitro expansion and in vivo manipulation of NP cells.
The molecular mechanism by which cancer-associated fibroblasts (CAFs) confer chemoresistance in ovarian cancer is poorly understood. The purpose of the present study was to evaluate the roles of CAFs ...in modulating tumor vasculature, chemoresistance, and disease progression. Here, we found that CAFs upregulated the lipoma-preferred partner (LPP) gene in microvascular endothelial cells (MECs) and that LPP expression levels in intratumoral MECs correlated with survival and chemoresistance in patients with ovarian cancer. Mechanistically, LPP increased focal adhesion and stress fiber formation to promote endothelial cell motility and permeability. siRNA-mediated LPP silencing in ovarian tumor-bearing mice improved paclitaxel delivery to cancer cells by decreasing intratumoral microvessel leakiness. Further studies showed that CAFs regulate endothelial LPP via a calcium-dependent signaling pathway involving microfibrillar-associated protein 5 (MFAP5), focal adhesion kinase (FAK), ERK, and LPP. Thus, our findings suggest that targeting endothelial LPP enhances the efficacy of chemotherapy in ovarian cancer. Our data highlight the importance of CAF-endothelial cell crosstalk signaling in cancer chemoresistance and demonstrate the improved efficacy of using LPP-targeting siRNA in combination with cytotoxic drugs.
Advanced high-grade serous ovarian cancer (HGSC) is an aggressive disease that accounts for 70% of all ovarian cancer deaths. Nevertheless, 15% of patients diagnosed with advanced HGSC survive more ...than 10 years. The elucidation of predictive markers of these long-term survivors (LTS) could help identify therapeutic targets for the disease, and thus improve patient survival rates. To investigate the stromal heterogeneity of the tumor microenvironment (TME) in ovarian cancer, we used spatial transcriptomics to generate spatially resolved transcript profiles in treatment-naïve advanced HGSC from LTS and short-term survivors (STS) and determined the association between cancer-associated fibroblasts (CAF) heterogeneity and survival in patients with advanced HGSC. Spatial transcriptomics and single-cell RNA-sequencing data were integrated to distinguish tumor and stroma regions, and a computational method was developed to investigate spatially resolved ligand-receptor interactions between various tumor and CAF subtypes in the TME. A specific subtype of CAFs and its spatial location relative to a particular ovarian cancer cell subtype in the TME correlated with long-term survival in patients with advanced HGSC. Also, increased APOE-LRP5 cross-talk occurred at the stroma-tumor interface in tumor tissues from STS compared with LTS. These findings were validated using multiplex IHC. Overall, this spatial transcriptomics analysis revealed spatially resolved CAF-tumor cross-talk signaling networks in the ovarian TME that are associated with long-term survival of patients with HGSC. Further studies to confirm whether such cross-talk plays a role in modulating the malignant phenotype of HGSC and could serve as a predictive biomarker of patient survival are warranted.
Generation of spatially resolved gene expression patterns in tumors from patients with ovarian cancer surviving more than 10 years allows the identification of novel predictive biomarkers and therapeutic targets for better patient management. See related commentary by Kelliher and Lengyel, p. 1383.
Affective computing (also referred to as artificial emotion intelligence or emotion AI) is the study and development of systems and devices that can recognize, interpret, process, and simulate ...emotion or other affective phenomena. With the rapid growth in the aging population around the world, affective computing has immense potential to benefit the treatment and care of late-life mood and cognitive disorders. For late-life depression, affective computing ranging from vocal biomarkers to facial expressions to social media behavioral analysis can be used to address inadequacies of current screening and diagnostic approaches, mitigate loneliness and isolation, provide more personalized treatment approaches, and detect risk of suicide. Similarly, for Alzheimer's disease, eye movement analysis, vocal biomarkers, and driving and behavior can provide objective biomarkers for early identification and monitoring, allow more comprehensive understanding of daily life and disease fluctuations, and facilitate an understanding of behavioral and psychological symptoms such as agitation. To optimize the utility of affective computing while mitigating potential risks and ensure responsible development, ethical development of affective computing applications for late-life mood and cognitive disorders is needed.
mTOR inhibitor rapamycin and its analogs are lipophilic, demonstrate blood–brain barrier penetration, and have shown promising antitumor effects in several types of refractory tumors. We thus try to ...explore the therapeutic effects of mTOR inhibitors on brain metastasis models. We examined the effects of different dose of mTOR inhibitors (rapamycin, Temsirolimus-CCI-779) on cell invasion in two brain metastatic breast cancer cell lines (MDA-MB231-BR and CN34-BrM2). Antibody microarray and immunoblotting were applied to detect signaling pathways underlying the dose differential drug effects. The in vivo effects of single drug (CCI-779), and drug combination of CCI-779 with SL327 (a brain penetrant MEK inhibitor) to eliminate the unfavorable activation of MAPK pathway were evaluated in MDA-MB231-BR brain metastases xenograft mice. The two mTOR inhibitors, rapamycin and CCI-779, inhibited the invasion of brain metastatic cells only at a moderate concentration level, which was lost at higher concentrations secondary to activation of the MAPK signaling pathway. Pharmacological inhibition of ERK1/2 by PD98059 and SL327 restored the anti-invasion effects of mTOR inhibition in vitro. In vivo, a significant decrease was noted in the average number of micro and large metastatic lesions as well as the whole brain GFP expression in the CCI-779 1 mg/kg/day treated group compared with that in the vehicle group (
P
< 0.05). However, 10 mg/kg CCI-779 treatment did not show significant anti-metastasis effect on the animal model. High-dose CCI-779 eliciting the ERK MAPK activation in the brain metastatic lesion was corroborated. Combined with the brain penetrant MEK inhibitor SL327, high-dose CCI-779 significantly reduces the brain metastasis, and the combination treatment prohibited perivascular invasion of tumor cells and inhibits tumor angiogenesis in vivo. This study provides evidence on the potential value of CCI-779 as well as CCI-779 + SL327 in prohibiting breast cancer brain metastasis.
Although age-related confluent white-matter lesion (WML) is an important substrate for cognitive impairment, the mechanisms whereby WML induces cognitive impairment are uncertain. The authors ...investigated cognitive predictors in patients with confluent WML.
Among 100 patients with ischaemic stroke with confluent WML on MRI, the authors assessed executive function and global cognition by the Mattis Dementia Rating Scale--Initiation/Perseveration Subscale (MDRS I/P) and Mini-Mental State Examination (MMSE), respectively. All volumetric measures were corrected for intracranial volume. The authors investigated the association between basic demography, vascular risk factors, APOE status, WML volume, infarct measures (volume, number, location), microbleed number, atrophy measures (global, central, regional) and cognitive performance. The authors also performed Pittsburgh Compound B (PIB) imaging among seven cognitive impaired patients with stroke.
WML was no longer related to cognitive performance after adding atrophy into regression equations. Multivariate regression models showed that cortical grey matter volume independently accounted for performance on both the MDRS I/P (β=0.241, p=0.045) and MMSE (β=0.243, p=0.032). Models examining frontal subregions revealed that volumes of both left (β=0.424, p<0.001) and right (β=0.219, p=0.045) lateral frontal orbital gyri predicted MDRS I/P, whereas education (β=0.385, p<0.001) and left lateral frontal orbital gyrus (β=0.222, p=0.037) predicted MMSE. Volumes of WML and cognitively relevant brain regions were significantly associated. Seven patients with PIB imaging showed no uptake pattern typical of Alzheimer's disease, suggesting a predominantly vascular aetiology for the cognitive impairment and brain changes in these patients.
Cognitive impairment in patients with confluent WML is mediated by global and frontal cortical atrophy.
Recently, the tract-based white matter (WM) fiber analysis has been recognized as an effective framework to study the diffusion tensor imaging (DTI) data of human brain. This framework can provide ...biologically meaningful results and facilitate the tract-based comparison across subjects. However, due to the lack of quantitative definition of WM bundle boundaries, the complexity of brain architecture and the variability of WM shapes, clustering WM fibers into anatomically meaningful bundles is nontrivial. In this paper, we propose a hybrid top-down and bottom-up approach for automatic clustering and labeling of WM fibers, which utilizes both brain parcellation results and similarities between WM fibers. Our experimental results show reasonably good performance of this approach in clustering WM fibers into anatomically meaningful bundles.
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