Variability between human pluripotent stem cell (hPSC) lines remains a challenge and opportunity in biomedicine. In this study, hPSC lines from multiple donors were differentiated toward neuroectoderm and mesendoderm lineages. We revealed dynamic transcriptomic patterns that delineate the emergence of these lineages, which were conserved across lines, along with individual line-specific transcriptional signatures that were invariant throughout differentiation. These transcriptomic signatures predicted an antagonism between SOX21-driven forebrain fates and retinoic acid-induced hindbrain fates. Replicate lines and paired adult tissue demonstrated the stability of these line-specific transcriptomic traits. We show that this transcriptomic variation in lineage bias had both genetic and epigenetic origins, aligned with the anterior-to-posterior structure of early mammalian development, and was present across a large collection of hPSC lines. These findings contribute to developing systematic analyses of PSCs to define the origin and consequences of variation in the early events orchestrating individual human development.

Biology, microscopy, and computer vision are part of a positive feedback ecosystem where progress in one provides opportunities and promotes advancement in the others. Unfortunately, development is normally responsive rather than coordinated.

Schizophrenia is a neurodevelopmental psychiatric disorder, encompassing genetic and environmental risk factors. For several decades, investigators have been implementing the use of lesions of the neonatal rodent hippocampus to model schizophrenia, resulting in a broad spectrum of adult schizophrenia-related behavioral changes. Despite the extensive use of these proposed animal models of schizophrenia, the mechanisms by which these lesions result in schizophrenia-like behavioral alterations remain unclear. Here we provide in vivo evidence that transient pharmacological inactivation of the hippocampus via tetrodotoxin microinjections or a genetic reduction in brain derived neurotrophic factor (BDNF) protein levels (BDNF+/- rats) lead to global DNA hypomethylation, disrupted maturation of the neuronal nucleus and aberrant acoustic startle response in the adult rat. The similarity between the effects of the two treatments strongly indicate that BDNF signaling is involved in effects obtained after the TTX microinjections. These findings may shed light on the cellular mechanisms underlying the phenotypical features of neonatal transient inhibition of the hippocampus as a preclinical model of schizophrenia and suggest that BDNF signaling represents a target pathway for development of novel treatment therapies.

Autism spectrum disorder (ASD) is often signaled by atypical cries during infancy. Copy number variants (CNVs) provide genetically identifiable cases of ASD, but how early atypical cries predict a later onset of ASD among CNV carriers is not understood in humans. Genetic mouse models of CNVs have provided a reliable tool to experimentally isolate the impact of CNVs and identify early predictors for later abnormalities in behaviors relevant to ASD. However, many technical issues have confounded the phenotypic characterization of such mouse models, including systematically biased genetic backgrounds and weak or absent behavioral phenotypes. To address these issues, we developed a coisogenic mouse model of human proximal 16p11.2 hemizygous deletion and applied computational approaches to identify hidden variables within neonatal vocalizations that have predictive power for postpubertal dimensions relevant to ASD. After variables of neonatal vocalizations were selected by least absolute shrinkage and selection operator (Lasso), random forest, and Markov model, regression models were constructed to predict postpubertal dimensions relevant to ASD. While the average scores of many standard behavioral assays designed to model dimensions did not differentiate a model of 16p11.2 hemizygous deletion and wild-type littermates, specific call types and call sequences of neonatal vocalizations predicted individual variability of postpubertal reciprocal social interaction and olfactory responses to a social cue in a genotype-specific manner. Deep-phenotyping and computational analyses identified hidden variables within neonatal social communication that are predictive of postpubertal behaviors.

Variability between human pluripotent stem cell (hPSC) lines remains a challenge and opportunity in biomedicine. We identified differences in the early lineage emergence across hPSC lines that mapped on the antero-posterior axis of embryonic development. RNA-seq analysis revealed dynamic transcriptomic patterns that defined the emergence of mesendodermal versus neuroectodermal lineages conserved across hPSC lines and cell line-specific transcriptional signatures that were invariant across differentiation. The stable cell line-specific transcriptomic patterns predicted the retinoic acid (RA) response of the cell lines, resulting in distinct bias towards fore-versus hind-brain fates. Replicate hPSC lines and paired adult donor tissue demonstrated that cells from individual humans expressed unique and long-lasting transcriptomic signatures associated with evolutionarily recent genes. In addition to this genetic contribution, we found that replicate lines from a single donor showed divergent brain regional fates linked to distinct chromatin states, indicating that epigenetic mechanisms also contribute to neural fate differences. This variation in lineage bias and its correlation with RA responsive gene expression was also observed in a large collection of hPSC lines. These results define transcriptomic differences in hPSCs that initiate a critical early step specifying anterior or posterior neural fates.

Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. In this paper, we propose a novel box-based instance segmentation method. Box-based instance segmentation methods capture objects via bounding boxes and then perform individual segmentation within each bounding box region. However, existing methods can hardly differentiate the target from its neighboring objects within the same bounding box region due to their similar textures and low-contrast boundaries. To deal with this problem, in this paper, we propose an object-guided instance segmentation method. Our method first detects the center points of the objects, from which the bounding box parameters are then predicted. To perform segmentation, an object-guided coarse-to-fine segmentation branch is built along with the detection branch. The segmentation branch reuses the object features as guidance to separate target object from the neighboring ones within the same bounding box region. To further improve the segmentation quality, we design an auxiliary feature refinement module that densely samples and refines point-wise features in the boundary regions. Experimental results on three biological image datasets demonstrate the advantages of our method. The code will be available at https://github.com/yijingru/ObjGuided-Instance-Segmentation.

The past 5 years have seen a sharp increase in the number of studies using calcium imaging in behaving rodents. These studies have helped identify important roles for individual cells, brain regions, and circuits in some of the core behavioral phenotypes of psychiatric disorders, such as schizophrenia and autism, and have characterized network dysfunction in well-established models of these disorders. Since rescuing clinically relevant behavioral deficits in disease model mice remains a foundation of preclinical CNS research, these studies have the potential to inform new therapeutic approaches targeting specific cell types or projections, or perhaps most importantly, the network-level context in which neurons function. In this mini-review, we will provide a brief overview of recent insights into psychiatric disease-associated mouse models and behavior paradigms, focusing on those achieved by cellular resolution imaging of calcium dynamics in neural populations. We will then discuss how these experiments can support efforts within the pharmaceutical industry, such as target identification, assay development, and candidate screening and validation. Calcium imaging is uniquely capable of bridging the gap between two of the key resources that currently enable CNS drug discovery: genomic and transcriptomic data from human patients, and translatable, population-resolution measures of brain activity (such as fMRI and EEG). Applying this knowledge could yield real value to patients in the near future.

Specific cell populations may have unique contributions to schizophrenia but may be missed in studies of homogenate tissue. Here laser capture microdissection followed by RNA sequencing (LCM-seq) was used to transcriptomically profile the granule cell layer of the dentate gyrus (DG-GCL) in human hippocampus and contrast these data to those obtained from bulk hippocampal homogenate. We identified widespread cell-type-enriched aging and genetic effects in the DG-GCL that were either absent or directionally discordant in bulk hippocampus data. Of the ~9 million expression quantitative trait loci identified in the DG-GCL, 15% were not detected in bulk hippocampus, including 15 schizophrenia risk variants. We created transcriptome-wide association study genetic weights from the DG-GCL, which identified many schizophrenia-associated genetic signals not found in transcriptome-wide association studies from bulk hippocampus, including GRM3 and CACNA1C. These results highlight the improved biological resolution provided by targeted sampling strategies like LCM and complement homogenate and single-nucleus approaches in human brain.

Instance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the objects, make instance segmentation a non-trivial task. Current box-free instance segmentation methods typically rely on local pixel-level information. Due to a lack of global object view, these methods are prone to over- or under-segmentation. On the contrary, the box-based instance segmentation methods incorporate object detection into the segmentation, performing better in identifying the individual instances. In this paper, we propose a new box-based instance segmentation method. Mainly, we locate the object bounding boxes from their center points. The object features are subsequently reused in the segmentation branch as a guide to separate the clustered instances within an RoI patch. Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects. Consequently, the proposed model performs excellently in segmenting the clustered objects while retaining the target object details. The proposed method achieves state-of-the-art performances on three biological datasets: cell nuclei, plant phenotyping dataset, and neural cells.

Better understanding of the progression of neural stem cells (NSCs) in the developing cerebral cortex is important for modeling neurogenesis and defining the pathogenesis of neuropsychiatric disorders. Here, we use RNA sequencing, cell imaging, and lineage tracing of mouse and human in vitro NSCs and monkey brain sections to model the generation of cortical neuronal fates. We show that conserved signaling mechanisms regulate the acute transition from proliferative NSCs to committed glutamatergic excitatory neurons. As human telencephalic NSCs develop from pluripotency in vitro, they transition through organizer states that spatially pattern the cortex before generating glutamatergic precursor fates. NSCs derived from multiple human pluripotent lines vary in these early patterning states, leading differentially to dorsal or ventral telencephalic fates. This work furthers systematic analyses of the earliest patterning events that generate the major neuronal trajectories of the human telencephalon.

Human induced pluripotent stem cells (hiPSCs) are a powerful model of neural differentiation and maturation. We present a hiPSC transcriptomics resource on corticogenesis from 5 iPSC donor and 13 subclonal lines across 9 time points over 5 broad conditions: self-renewal, early neuronal differentiation, neural precursor cells (NPCs), assembled rosettes, and differentiated neuronal cells. We identify widespread changes in the expression of both individual features and global patterns of transcription. We next demonstrate that co-culturing human NPCs with rodent astrocytes results in mutually synergistic maturation, and that cell type-specific expression data can be extracted using only sequencing read alignments without cell sorting. We lastly adapt a previously generated RNA deconvolution approach to single-cell expression data to estimate the relative neuronal maturity of iPSC-derived neuronal cultures and human brain tissue. Using many public datasets, we demonstrate neuronal cultures are maturationally heterogeneous but contain subsets of neurons more mature than previously observed.

Most existing methods handle cell instance segmentation problems directly without relying on additional detection boxes. These methods generally fails to separate touching cells due to the lack of global understanding of the objects. In contrast, box-based instance segmentation solves this problem by combining object detection with segmentation. However, existing methods typically utilize anchor box-based detectors, which would lead to inferior instance segmentation performance due to the class imbalance issue. In this paper, we propose a new box-based cell instance segmentation method. In particular, we first detect the five pre-defined points of a cell via keypoints detection. Then we group these points according to a keypoint graph and subsequently extract the bounding box for each cell. Finally, cell segmentation is performed on feature maps within the bounding boxes. We validate our method on two cell datasets with distinct object shapes, and empirically demonstrate the superiority of our method compared to other instance segmentation techniques. Code is available at: https://github.com/yijingru/KG_Instance_Segmentation.

Laser capture microdissection followed by RNA-seq (LCM-seq) was used to profile the transcriptional landscape of the granule cell layer of the dentate gyrus (DG-GCL) in human hippocampus, and contrasted to homogenate tissue. We identified widespread cell type-specific aging and genetic effects in the DG-GCL that were either missing or directionally discordant in corresponding bulk hippocampus RNA-seq data from largely the same subjects. Of the ∼9 million eQTLs in the DG-GCL, 15% were not in bulk hippocampus, including 15 schizophrenia genome-wide association study (GWAS) risk variants. We then created custom transcriptome-wide association study (TWAS) genetic weights from the DG-GCL which identified many novel schizophrenia-associated genetic signals not found in TWAS from bulk hippocampus, including GRM3 and CACNA1C. These results highlight the biological resolution of cell type-specific expression profiling using targeted sampling strategies like LCM, and complement homogenate and single nuclei approaches in human brain.

Instance segmentation of neural cells plays an important role in brain study. However, this task is challenging due to the special shapes and behaviors of neural cells. Existing methods are not precise enough to capture their tiny structures, e.g., filopodia and lamellipodia, which are critical to the understanding of cell interaction and behavior. To this end, we propose a novel deep multi-task learning model to jointly detect and segment neural cells instance-wise. Our method is built upon SSD, with ResNet101 as the backbone to achieve both high detection accuracy and fast speed. Furthermore, unlike existing works which tend to produce wavy and inaccurate boundaries, we embed a deconvolution module into SSD to better capture details. Experiments on a dataset of neural cell microscopic images show that our method is able to achieve better performance in terms of accuracy and efficiency, comparing favorably with current state-of-the-art methods.

Neural cell instance segmentation, which aims at joint detection and segmentation of every neural cell in a microscopic image, is essential to many neuroscience applications. The challenge of this task involves cell adhesion, cell distortion, unclear cell contours, low-contrast cell protrusion structures, and background impurities. Consequently, current instance segmentation methods generally fall short of precision. In this paper, we propose an attentive instance segmentation method that accurately predicts the bounding box of each cell as well as its segmentation mask simultaneously. In particular, our method builds on a joint network that combines a single shot multi-box detector (SSD) and a U-net. Furthermore, we employ the attention mechanism in both detection and segmentation modules to focus the model on the useful features. The proposed method is validated on a dataset of neural cell microscopic images. Experimental results demonstrate that our approach can accurately detect and segment neural cell instances at a fast speed, comparing favorably with the state-of-the-art methods. Our code is released on GitHub. The link is https://github.com/yijingru/ANCIS-Pytorch.

The hippocampus formation, although prominently implicated in schizophrenia pathogenesis, has been overlooked in large-scale genomics efforts in the schizophrenic brain. We performed RNA-seq in hippocampi and dorsolateral prefrontal cortices (DLPFCs) from 551 individuals (286 with schizophrenia). We identified substantial regional differences in gene expression and found widespread developmental differences that were independent of cellular composition. We identified 48 and 245 differentially expressed genes (DEGs) associated with schizophrenia within the hippocampus and DLPFC, with little overlap between the brain regions. 124 of 163 (76.6%) of schizophrenia GWAS risk loci contained eQTLs in any region. Transcriptome-wide association studies in each region identified many novel schizophrenia risk features that were brain region-specific. Last, we identified potential molecular correlates of in vivo evidence of altered prefrontal-hippocampal functional coherence in schizophrenia. These results underscore the complexity and regional heterogeneity of the transcriptional correlates of schizophrenia and offer new insights into potentially causative biology.

Existing neural cell tracking methods generally use the morphology cell features for data association. However, these features are limited to the quality of cell segmentation and are prone to errors for mitosis determination. To overcome these issues, in this work we propose an online multi-object tracking method that leverages both cell appearance and motion features for data association. In particular, we propose a supervised blob-seed network (BSNet) to predict the cell appearance features and an unsupervised optical flow network (UnFlowNet) for capturing the cell motions. The data association is then solved using the Hungarian algorithm. Experimental evaluation shows that our approach achieves better performance than existing neural cell tracking methods.

The visual inspection of pluripotent stem cell colonies by microscopy is widely used as a primary method to assess the quality of the preparations and degree of pluripotency. The lack of ground truth and the possible inconsistency of evaluations from multiple experts within and between stem cell laboratories are sources of uncertainty about the state of the cells, the reproducibility of preparations, and the efficiency of expansion protocols. To examine how to evaluate the level of confidence one has in disparate rating from experts, we explored a statistical method for assessing the differences in ratings of pluripotent stem cells by two different experts. Two experts rated phase contrast microscope images of human embryonic stem cell (hESC) colonies on a scale of 1 (poor) to 5 (maximum pluripotency character) but agreed with one another only 48% of the time. To assess whether experts used similar criteria to rate colonies, we developed custom image feature algorithms based on the stated visual criteria provided by the experts for selection of colonies. These features, plus others, were then used to develop pluripotency scoring algorithms trained to reflect ratings of both experts. We treated expert ratings as inexact indicators of a continuous pluripotency score and considered the inconsistency between expert ratings in developing our models. The model suggests that the two experts use somewhat different scales for discriminating between colony quality. Covariance analysis indicated that both experts use features that are not included in the model. Two image features, colony perimeter and a feature based on texture, were the most important for both experts for predicting the ratings. Interestingly, colony perimeter was not one of the expert-provided criteria for rating colonies, showing that this modeling approach allowed identification of features that the experts were not aware they were using. A linear model based on both experts identified each expert’s top-rated colonies as well as, or better than, the ratings of the other expert, as indicated by receiver operator characteristic curve analysis. By providing an understanding of the differences and similarities in disparate sets of expert ratings, this analysis helps to establish confidence in the ratings and the criteria for ratings, even when the experts disagree.