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Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we develop STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit athttps://github.com/JEFworks-Lab/STalignand as Supplementary Software with additional documentation and tutorials available athttps://jef.works/STalign.

 

In this paper we propose a novel neurostimulation protocol that provides an intervention-based assessment to distinguish the contributions of different motor control networks in the cortico-spinal system. Specifically, we use a combination of non-invasive brain stimulation and neuromuscular stimulation to probe neuromuscular system behavior with targeted impulse-response system identification. In this protocol, we use an in-house developed human-machine interface (HMI) for an isotonic wrist movement task, where the user controls a cursor on-screen. During the task, we generate unique motor evoked potentials based on triggered cortical or spinal level perturbations. Externally applied brain-level perturbations are triggered through TMS to cause wrist flexion/extension during the volitional task. The resultant contraction output and related reflex responses are measured by the HMI. These movements also include neuromodulation in the excitability of the brain-muscle pathway via transcranial direct current stimulation. Colloquially, spinal-level perturbations are triggered through skin-surface neuromuscular stimulation of the wrist muscles. The resultant brain-muscle and spinal-muscle pathways perturbed by the TMS and NMES, respectively, demonstrate temporal and spatial differences as manifested through the human-machine interface. This then provides a template to measure the specific neural outcomes of the movement tasks, and in decoding differences in the contribution of cortical- (long-latency) and spinal-level (short-latency) motor control. This protocol is part of the development of a diagnostic tool that can be used to better understand how interaction between cortical and spinal motor centers changes with learning, or injury such as that experienced following stroke.

 

Abstract Recent advances in brain clearing and imaging have made it possible to image entire mammalian brains at sub-micron resolution. These images offer the potential to assemble brain-wide atlases of neuron morphology, but manual neuron reconstruction remains a bottleneck. Several automatic reconstruction algorithms exist, but most focus on single neuron images. In this paper, we present a probabilistic reconstruction method, ViterBrain, which combines a hidden Markov state process that encodes neuron geometry with a random field appearance model of neuron fluorescence. ViterBrain utilizes dynamic programming to compute the global maximizer of what we call the most probable neuron path. We applied our algorithm to imperfect image segmentations, and showed that it can follow axons in the presence of noise or nearby neurons. We also provide an interactive framework where users can trace neurons by fixing start and endpoints. ViterBrain is available in our open-source Python package . 

Neuromorphology is crucial to identifying neuronal subtypes and understanding learning. It is also implicated in neurological disease. However, standard morphological analysis focuses on macroscopic features such as branching frequency and connectivity between regions, and often neglects the internal geometry of neurons. In this work, we treat neuron trace points as a sampling of differentiable curves and fit them with a set of branching B-splines. We designed our representation with the Frenet-Serret formulas from differential geometry in mind. The Frenet-Serret formulas completely characterize smooth curves, and involve two parameters, curvature and torsion. Our representation makes it possible to compute these parameters from neuron traces in closed form. These parameters are defined continuously along the curve, in contrast to other parameters like tortuosity which depend on start and end points. We applied our method to a dataset of cortical projection neurons traced in two mouse brains, and found that the parameters are distributed differently between primary, collateral, and terminal axon branches, thus quantifying geometric differences between different components of an axonal arbor. The results agreed in both brains, further validating our representation. The code used in this work can be readily applied to neuron traces in SWC format and is available in our open-source Python package brainlit : http://brainlit.neurodata.io/ . 

Somatic mosaicism is defined as an occurrence of two or more populations of cells having genomic sequences differing at given loci in an individual who is derived from a single zygote. It is a characteristic of multicellular organisms that plays a crucial role in normal development and disease. To study the nature and extent of somatic mosaicism in autism spectrum disorder, bipolar disorder, focal cortical dysplasia, schizophrenia, and Tourette syndrome, a multi-institutional consortium called the Brain Somatic Mosaicism Network (BSMN) was formed through the National Institute of Mental Health (NIMH). In addition to genomic data of affected and neurotypical brains, the BSMN also developed and validated a best practices somatic single nucleotide variant calling workflow through the analysis of reference brain tissue. These resources, which include >400 terabytes of data from 1087 subjects, are now available to the research community via the NIMH Data Archive (NDA) and are described here.

 

Abstract Background Human mesenchymal stem cells (hMSCs) are intensely researched for applications in cell therapeutics due to their unique properties, however, intrinsic therapeutic properties of hMSCs could be enhanced by genetic modification. Viral transduction is efficient, but suffers from safety issues. Conversely, nonviral gene delivery, while safer compared to viral, suffers from inefficiency and cytotoxicity, especially in hMSCs. To address the shortcomings of nonviral gene delivery to hMSCs, our lab has previously demonstrated that pharmacological ‘priming’ of hMSCs with the glucocorticoid dexamethasone can significantly increase transfection in hMSCs by modulating transfection-induced cytotoxicity. This work seeks to establish a library of transfection priming compounds for hMSCs by screening 707 FDA-approved drugs, belonging to diverse drug classes, from the NIH Clinical Collection at four concentrations for their ability to modulate nonviral gene delivery to adipose-derived hMSCs from two human donors. Results Microscope images of cells transfected with a fluorescent transgene were analyzed in order to identify compounds that significantly affected hMSC transfection without significant toxicity. Compound classes that increased transfection across both donors included glucocorticoids, antibiotics, and antihypertensives. Notably, clobetasol propionate, a glucocorticoid, increased transgene production 18-fold over unprimed transfection. Furthermore, compound classes that decreased transfection across both donors included flavonoids, antibiotics, and antihypertensives, with the flavonoid epigallocatechin gallate decreasing transgene production − 41-fold compared to unprimed transfection. Conclusions Our screen of the NCC is the first high-throughput and drug-repurposing approach to identify nonviral gene delivery priming compounds in two donors of hMSCs. Priming compounds and classes identified in this screen suggest that modulation of proliferation, mitochondrial function, and apoptosis is vital for enhancing nonviral gene delivery to hMSCs. 

Range expansions—whether permanent or transient—strongly influence the distribution of genetic variation in space. Monarch butterflies are best known for long‐distance seasonal migration within North America but are also established as nonmigratory populations around the world, including on Pacific Islands. Previous research has highlighted stepwise expansion across the Pacific, though questions remain about expansion timing and the population genetic consequences of migration loss. Here, we present reduced‐representation sequencing data for 275 monarchs from North America (n = 85), 12 Pacific Islands (n = 136) and three locations in Australia (n = 54), with the goal of understanding (i) how the monarch's Pacific expansion has shaped patterns of population genetic variation and (ii) how loss of migration has influenced spatial patterns of differentiation. We find support for previously described stepwise dispersal across the Pacific and document an additional expansion from Hawaii into the Mariana Islands. Nonmigratory monarchs within the Mariana Islands show strong patterns of differentiation, despite their proximity; by contrast, migratory North American samples form a single genetically panmictic population across the continent. Estimates of Pacific establishment timing are highly uncertain (~100–1,000,000 years ago) but overlap with historical records that indicate a recent expansion. Our data support (i) a recent expansion across the Pacific whose timing overlaps with available historical records of establishment and (ii) a strong role for seasonal migration in determining patterns of spatial genetic variation. Our results are noteworthy because they demonstrate how the evolution of partial migration can drive population differentiation over contemporary timescales.

 

Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated that signals such as pulse rate and respiration rate can be extracted from digital video of humans, increasing the possibility of contact-less monitoring. This paper presents a novel approach to obtaining physiological signals and classifying stress states from thermal video. The proposed network–”StressNet”–features a hybrid emission representation model that models the direct emission and absorption of heat by the skin and underlying blood vessels. This results in an information-rich feature representation of the face, which is used by spatio-temporal network for reconstructing the ISTI ( Initial Systolic Time Interval : a measure of change in cardiac sympathetic activity that is considered to be a quantitative index of stress in humans). The reconstructed ISTI signal is fed into a stress-detection model to detect and classify the individual’s stress state (i.e. stress or no stress). A detailed evaluation demonstrates that StressNet achieves estimated the ISTI signal with 95% accuracy and detect stress with average precision of 0.842.