Biology subjects

Attili, S. M.

Publications and source records attributed to Attili, S. M..

4 recordsLinked to original sources

bioRxiv2021

Quantification of neuron types in the rodent hippocampal formation by data mining and numerical optimization

Quantifying the population sizes of distinct neuron types in different anatomical regions is an essential step towards establishing a brain cell census. Although estimates exist for the total neuronal populations in different species, the number and definition of each specific neuron type are still intensively investigated. Hippocampome.org is an open-source knowledge base with morphological, physiological, and molecular information for 122 neuron types in the rodent hippocampal formation. While such framework identifies all known neuron types in this system, their relative abundances remain largely unknown. This work quantitatively estimates the counts of all Hippocampome.org neuron types by literature mining and numerical optimization. We report the number of neurons in each type identified by main neurotransmitter (glutamate or GABA) and axonal-dendritic patterns throughout 26 subregions and layers of the dentate gyrus, Ammons horn, subiculum, and entorhinal cortex. We produce by sensitivity analysis reliable numerical ranges for each type and summarize the amounts across broad neuronal families defined by biomarkers expression and firing dynamics. Study of density distributions indicates that the number of dendritic-targeting interneurons, but not of other neuronal classes, is independent of anatomical volumes. All extracted values, experimental evidence, and related software code are released on Hippocampome.org.

neuroscience↗

bioRxiv2020

Cellular Anatomy of the Mouse Primary Motor Cortex

An essential step toward understanding brain function is to establish a cellular-resolution structural framework upon which multi-scale and multi-modal information spanning molecules, cells, circuits and systems can be integrated and interpreted. Here, through a collaborative effort from the Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based description of one brain structure - the primary motor cortex upper limb area (MOp-ul) of the mouse. Applying state-of-the-art labeling, imaging, computational, and neuroinformatics tools, we delineated the MOp-ul within the Mouse Brain 3D Common Coordinate Framework (CCF). We defined over two dozen MOp-ul projection neuron (PN) types by their anterograde targets; the spatial distribution of their somata defines 11 cortical sublayers, a significant refinement of the classic notion of cortical laminar organization. We further combine multiple complementary tracing methods (classic tract tracing, cell type-based anterograde, retrograde, and transsynaptic viral tracing, high-throughput BARseq, and complete single cell reconstruction) to systematically chart cell type-based MOp input-output streams. As PNs link distant brain regions at synapses as well as host cellular gene expression, our construction of a PN type resolution MOp-ul wiring diagram will facilitate an integrated analysis of motor control circuitry across the molecular, cellular, and systems levels. This work further provides a roadmap towards a cellular resolution description of mammalian brain architecture.

neuroscience↗

bioRxiv2019

Cell numbers, distribution, shape, and regional variation throughout the murine hippocampal formation from the adult brain Allen Reference Atlas

Quantifying the distribution of cells in every brain region is fundamental to attaining a comprehensive census of distinct neuronal and glial types. Until recently, estimating neuron numbers involved time-consuming procedures that were practically limited to stereological sampling. Progress in open-source image recognition software, growth in computing power, and unprecedented neuroinformatics developments now offer the potentially paradigm-shifting alternative of comprehensive cell-by-cell analysis in an entire brain region. The Allen Brain Atlas provides free digital access to complete series of raw Nissl-stained histological section images along with regional delineations. Automated cell segmentation of these data enables reliable and reproducible high-throughput quantification of regional variations in cell count, density, size, and shape at whole-system scale. While this strategy is directly applicable to any regions of the mouse brain, we first deploy it here on the closed-loop circuit of the hippocampal formation: the medial and lateral entorhinal cortices; dentate gyrus (DG); areas Cornu Ammonis 3 (CA3), CA2, and CA1; and dorsal and ventral subiculum. Using two independent image processing pipelines and the adult mouse reference atlas, we report the first cellular-level soma segmentation in every sub-region and layer of the left hippocampal formation through the full rostral-caudal extent, except for the (already well characterized) principal layers of CA and DG. The overall numbers ([~]600k cells in entorhinal cortex, [~]200k in DG, [~]430k in CA1-3, and [~]290k in subiculum) are corroborated by traditional stereological sampling on a data subset and well match sparse published reports.

neuroscience↗

bioRxiv2019

Operations Research Methods for Estimating the Population Size of Neuron Types

Understanding brain computation requires assembling a complete catalog of its architectural components. Although the brain is organized into several anatomical and functional regions, it is ultimately the neurons in every region that are responsible for cognition and behavior. Thus, classifying neuron types through-out the brain and quantifying the population sizes of distinct classes in different regions is a key subject of research in the neuroscience community. Although the total number of neurons in the brain has been estimated for multiple species, the definition and population size of each neuron type are still open questions even in common model organisms: the so called cell census problem. We propose a methodology that uses operations research principles to estimate the number of neurons in each type based on available information on their distinguishing properties. Thus, assuming a set of neuron type definitions, we provide a solution to the issue of assessing their relative proportions. Specifically, we present a three-step approach that includes literature search, equation generation, and numerical optimization. Solving numerically the set of equations generated by literature mining yields best estimates or most likely ranges for the number of neurons in each type. While this strategy can be applied to any neural system, we illustrate its usage on the rodent hippocampus.

neuroscience↗