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Electric fields affect the activity of neurons and brain circuits, yet how this interaction happens at the cellular level remains enigmatic. Lack of understanding on how to stimulate the human brain to promote or suppress specific activity patterns significantly limits basic research and clinical applications. Here we study how electric fields impact the subthreshold and spiking properties of major cortical neuronal classes. We find that cortical neurons in rodent neocortex and hippocampus as well as human cortex exhibit strong and cell class-dependent entrainment that depends on the stimulation frequency. Excitatory pyramidal neurons with their typically slower spike rate entrain to slow and fast electric fields, while inhibitory classes like Pvalb and SST with their fast spiking predominantly phase lock to fast fields. We show this spike-field entrainment is the result of two effects: non-specific membrane polarization occurring across classes and class-specific excitability properties. Importantly, these properties of spike-field and class-specific entrainment are present in cells across cortical areas and species (mouse and human). These findings open the door to the design of selective and class-specific neuromodulation technologies.

Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. While methods to perform multimodal measurements in the same set of single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. Here, we present an optimization framework to learn coordinated representations of multimodal data, and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction, and identifies cell types that are consistent across modalities.HighlightsCoupled autoencoders for multimodal assignment, Analysis of Patch-seq data consisting of more than 3000 cells

SUMMARYThe isocortex and hippocampal formation are two major structures in the mammalian brain that play critical roles in perception, cognition, emotion and learning. Both structures contain multiple regions, for many of which the cellular composition is still poorly understood. In this study, we used two complementary single-cell RNA-sequencing approaches, SMART-Seq and 10x, to profile ∼1.2 million cells covering all regions in the adult mouse isocortex and hippocampal formation, and derived a cell type taxonomy comprising 379 transcriptomic types. The completeness of coverage enabled us to define gene expression variations across the entire spatial landscape without significant gaps. We found that cell types are organized in a hierarchical manner and exhibit varying degrees of discrete or continuous relatedness with each other. Such molecular relationships correlate strongly with the spatial distribution patterns of the cell types, which can be region-specific, or shared across multiple regions, or part of one or more gradients along with other cell types. Glutamatergic neuron types have much greater diversity than GABAergic neuron types, both molecularly and spatially, and they define regional identities as well as inter-region relationships. For example, we found that glutamatergic cell types between the isocortex and hippocampal formation are highly distinct from each other yet possess shared molecular signatures and corresponding layer specificities, indicating their homologous relationships. Overall, our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation for the first time, and begins to shed light on its underlying relationship with the development, evolution, connectivity and function of these two brain structures.

Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. We found that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features. We observed that these properties could vary continuously between t-types, which limited the ability to predict specific t-types from other data modalities. Despite that, the data support the presence of at least 20 interneuron met-types that have congruent morphological, electrophysiological, and transcriptomic properties.HighlightsPatch-seq data obtained from >3,700 GABAergic cortical interneuronsComprehensive characterization of morpho-electric features of transcriptomic types20 interneuron met-types that have congruent properties across data modalitiesDifferent Sst met-types preferentially innervate different cortical layers

Neurons are frequently classified into distinct groups or cell types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 3,700 GABAergic mouse visual cortical neurons and reconstructed the local morphologies of 350 of those neurons. We found that most transcriptomic types (t-types) occupy specific laminar positions within mouse visual cortex, and many of those t-types exhibit consistent electrophysiological and morphological features. We observed that these properties could vary continuously between t- types, which limited the ability to predict specific t-types from other data modalities. Despite that, the data support the presence of at least 20 interneuron met-types that have congruent morphological, electrophysiological, and transcriptomic properties.

The isocortex and hippocampal formation are two major structures in the mammalian brain that play critical roles in perception, cognition, emotion and learning. Using single-cell RNA-sequencing approaches, we profiled ~1.2 million cells covering all regions in the adult mouse isocortex and hippocampal formation. The cell types are organized hierarchically and exhibit varying degrees of discrete or continuous variations. Such molecular relationships correlate strongly with the spatial distribution patterns of the cell types, which can be region-specific, shared across multiple regions, or part of one or more gradients. Glutamatergic neuron types display much greater diversity than GABAergic neuron types, both molecularly and spatially, and define regional identities as well as inter-region relationships. Our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation for the first time, and begins to shed light on its underlying relationship with the development, evolution, connectivity and function of these two brain structures.

The isocortex and hippocampal formation (HPF) in the mammalian brain play critical roles in perception, cognition, emotion, and learning. We profiled ∼1.3 million cells covering the entire adult mouse isocortex and HPF and derived a transcriptomic cell-type taxonomy revealing a comprehensive repertoire of glutamatergic and GABAergic neuron types. Contrary to the traditional view of HPF as having a simpler cellular organization, we discover a complete set of glutamatergic types in HPF homologous to all major subclasses found in the six-layered isocortex, suggesting that HPF and the isocortex share a common circuit organization. We also identify large-scale continuous and graded variations of cell types along isocortical depth, across the isocortical sheet, and in multiple dimensions in hippocampus and subiculum. Overall, our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation and begins to shed light on its underlying relationship with the development, evolution, connectivity, and function of these two brain structures.

The presence of different heterogeneous surfaces can directly influence the nucleation kinetics, crystal growth, and morphology of active pharmaceutical ingredients (APIs). However, a mechanistic understanding of heterogeneous nucleation remains lacking. Herein, we report the use of biocompatible crystalline heterogeneous surfaces to enhance the nucleation rates of the model API compound acetaminophen (APAP). We also report experimental and computational studies of the epitaxial growth mechanism of APAP on different substrates. Five crystalline substrates, namely, d-galactose (DGAL), the α and β forms of d-mannitol (DMAN), α-lactose monohydrate (LMH), and xylitol (XYL) were selected because they contain a similar functionality: a high density of hydroxyl groups per molecule. We measured the induction times in the presence of the substrates and used the results to rank the substrates based on their ability to enhance the nucleation of APAP. While all selected substrates enhanced the nucleation rates, XYL w...

Nucleation kinetics of small-molecule organics can be directed by heterogeneous surfaces imprinted with angular nanopatterns. In this work, we utilized biocompatible polymer thin films to enhance the nucleation rates of paracetamol (APAP). We found that flat polymer films without nanoimprinting enabled crystallization of APAP two times faster than bulk crystallization. Nanoimprinting of polymer films led to further enhancement of the nucleation rates. Polymer films imprinted with a nanopattern containing 40° angles were the most effective and enabled crystallization of APAP four times faster than bulk crystallization and two times faster than crystallization with flat polymer films. The films imprinted with the nanopatterns containing 60, 65, 80, or 90° angles were more effective in enhancing crystallization of APAP than the flat films but less effective compared to the 40° nanopattern. We also performed molecular dynamics simulations and an analysis of the hydrogen bonding between crystal faces and the p...

There is growing interest in the description of short-lived patterns in the spatiotemporal cortical activity monitored via neuroimaging. Most traditional analysis methods, designed to estimate relatively long-term brain dynamics, are not always appropriate to capture these patterns. Here we introduce a novel data-driven approach for detecting short-lived fMRI brain activity patterns. Exploiting Density Peak Clustering (Rodriguez and Laio [2014]), our approach reveals well localized clusters by identifying and grouping together voxels whose time-series are similar, irrespective of their brain location, even when very short time windows (∼10 volumes) are used. The method, which we call Coherence Density Peak Clustering (CDPC), is first tested on simulated data and compared with a standard unsupervised approach for fMRI analysis, independent component analysis (ICA). CDPC identifies activated voxels with essentially no false-positives and proves more reliable than ICA, which is troubled by a number of false positives comparable to that of true positives. The reliability of the method is demonstrated on real fMRI data from a simple motor task, containing brief iterations of the same movement. The clusters identified are found in regions expected to be involved in the task, and repeat synchronously with the paradigm. The methodology proposed is especially suitable for the study of short-time brain dynamics and single trial experiments, where the event or task of interest cannot be repeated for the same subject, as happens, for instance, in problem-solving, learning and decision-making. A GUI implementation of our method is available for download at https://github.com/micheleallegra/CDPC. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis, we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.

The free energy landscape theory has been very successful in rationalizing the folding behaviour of globular proteins, as this representation provides intuitive information on the number of states involved in the folding process, their populations and pathways of interconversion. We extend here this formalism to the case of the Aβ40 peptide, a 40-residue intrinsically disordered protein fragment associated with Alzheimer’s disease. By using an advanced sampling technique that enables free energy calculations to reach convergence also in the case of highly disordered states of proteins, we provide a precise structural characterization of the free energy landscape of this peptide. We find that such landscape has inverted features with respect to those typical of folded proteins. While the global free energy minimum consists of highly disordered structures, higher free energy regions correspond to a large variety of transiently structured conformations with secondary structure elements arranged in several different manners, and are not separated from each other by sizeable free energy barriers. From this peculiar structure of the free energy landscape we predict that this peptide should become more structured and not only more compact, with increasing temperatures, and we show that this is the case through a series of biophysical measurements., CINECA

Starting from a disordered aggregate, we have simulated the formation of ordered amyloid-like beta structures in a system formed by 18 polyvaline chains in explicit solvent, employing molecular dynamics accelerated by bias-exchange metadynamics. We exploited 8 different collective variables to compute the free energy of hundreds of putative aggregate structures, with variable content of parallel and antiparallel beta-sheets and different packing among the sheets. This allowed characterizing in detail a possible nucleation pathway for the formation of amyloid fibrils: first the system forms a relatively large ordered nucleus of antiparallel beta-sheets, and then a few parallel sheets start appearing. The relevant nucleation process culminates at this point: when a sufficient number of parallel sheets is formed, the free energy starts to decrease toward a new minimum in which this structure is predominant. The complex nucleation pathway we found cannot be described within classical nucleation theory, namely employing a unique simple reaction coordinate like the total content of beta-sheets.

Abeta40, the 40-residue form of Amyloid beta, is an intrinsically disordered protein which is involved in Alzheimer's disease and prone to form fibrils.We explored its conformational landscape by atomistic simulations in explicit solvent and NMR-guided metadynamics, an enhanced sampling technique based on the experimental chemical shifts data that allows predicting the relative free energy of the different states involved in the folding process of a protein.This approach provides a picture in striking consistency with experimental NMR measurements, with the global free energy minimum consisting of non-compact and disordered structures.However, the free energy landscape includes also a large amount of partially folded conformations with a relative high secondary structure content and a free energy only 3 kcal/mol higher than the disordered minimum. This structural characterization of Abeta40 provides a library of possible metastable states which can be involved in the aggregation process and used as targets for docking studies to design inhibitors to bind the Abeta fragments in order to prevent their fibrillation.

Starting from a disordered aggregate, we have simulated the formation of ordered amyloid-like beta structures in a system formed by 18 poly-valine chains in explicit solvent, by employing molecular dynamics accelerated by bias-exchange metadynamics. We exploited 8 different collective variables to compute the free energy of hundreds of putative aggregate structures, with variable content of parallel and anti-parallel beta-sheets and different packing among the sheets. This allowed characterizing in detail a possible nucleation pathway for the formation of amyloid fibrils: first the system forms a relatively large ordered nucleus of anti-parallel beta-sheets, then a few parallel sheets start appearing. The relevant nucleation process culminates at this point: when a sufficient number of parallel sheets is formed, the free energy starts to decrease towards a new minimum in which this structure is predominant. The complex nucleation pathway we found cannot be described within classical nucleation theory, namely employing a unique simple reaction coordinate like the total content of beta-sheets.

By extended atomistic simulations in explicit solvent and bias-exchange metadynamics, we study the aggregation process of 18 chains of the C-terminal segment of amyloid-beta, an intrinsically disordered protein involved in Alzheimer's disease and prone to form fibrils. Starting from a disordered aggregate, we are able to observe the formation of an ordered nucleus rich in beta sheets. The rate limiting step in the nucleation pathway involves crossing a barrier of approximately 40 kcal/mol and is associated with the formation of a very specific interdigitation of the side chains belonging to different sheets. This structural pattern is different from the one observed experimentally in a microcrystal of the same system, indicating that the structure of a "nascent'' fibril may differ from the one of an "extended'' fibril. DOI: 10.1103/PhysRevLett.110.168103