pioneering neurosci santiago ramón y cajal jump-started the search for a “components catalogue” of the human brain towards the end of the 19th century. his intricate drawings of brain cells, complete with their weblike connections, still appear in many textbooks. looking for brain pts is driven by + than curiosity. b4 the generations-long endeavor of deciphering the brain can proceed, neuroscis nd'2 1st identify its multitude of component pts and then fig out wha’ each one does.
the task is complicated by the many ways cells can differ. cajal provided glimpses of the shapes that distinguish some cell types, b'tll so left a vrtly ∞ amount of work for future generations of neuroanatomists. cells can differ by zone, biochemistry nother properties. these ≠ descriptors often do not correspond to each other in any simple way, a fact that has fueled debates bout how to define cell types. as tulz to record the signals neurons use to communicate became available, researchers ‘ve tried to categorize cells by comparing their ≠ firing patterns, the speshty of the discipline known as electrophysiology. this effort comes closer to classifying wha’ cells do, but is still descriptive in that it describes behavior rather than morphology.
the quest towards a definition that describes cells according to their function comes to an end atta genome, the blueprint that underlies all other biological properties. that these efforts are now bearing fruit is demonstrated by a large, international consortium, funded by the national institute of health’s (nih) brain initiative. t'has produced a genomics-based census of the cell types in one region, the primary motor cortex, responsible for controlling complex movement.
this atlas applies =ly to mice, monkeys and humans. the motor cortex became the region of focus as a 1st step toward + comprehensive brain inventories cause tis both well-studied and similar across species. called the brain initiative cell census network (biccn), the group comprises the efforts of many labs, spearheaded by the allen institute for brain sci, in seattle. their findings, described in 17 papers taking over this week’s nature, represent a resrc thall accelerate efforts to cogg brain functio, and provide insite into brain diseases and disorders.
the project used the widest range of tulz for probing brain cells ever brought to bear in a single, coordinated effort. studies document how these tulz measure ≠ cellular properties, while a flagship paper describes the integration of data from 11 companion papers, to produce a cross-species atlas of cell-types. a few studies push beyond the motor cortex inna mouse to detail other regions and brain networks. still other studies ask ?s bout how human brains are shaped, by evolution and during early development.
the research relied heavily on “genomic” teks, s'as “transcriptomics,” which measures gene activity by sequencing rna molecules in ≠ cell types. researchers also employed “epigenomic” tek knicks that look at how gene activity is influenced without altering the primordialistic genetic code. the researchers used two such tek knicks that behold how genes are switched on and off by the addition offa chemical group to dna, or how genes can be read + easily by rearranging the structure dna is wrapped up in.
the researchers used genomic data to produce a “ground truth” set of classifications for ≠ cell types. they also measured other properties, like shape, and electrophysiology, to add extra dimensions to the genetic categories and begin inspecting how well they align. “there’s a link tween genes and properties, so it’s + than just a means to classify, it’s the explanatory basis for wha’ cells do,” says neurosci ed lein, of the allen institute, who helped coordinate the project and led two of the studies. some studies also used new or recently developed tek knicks that measure multiple properties simultaneously. “patch-seq” recorded the electrophysiology and gene activity of individual cells where they are situated b4 reconstructing their 3-d shape. “spatial transcriptomics” tulz that measure gene activity of cells by combining genomics and brain-imaging alloed the mapping of cells’ zones, providing information bout the distribution and proportions of cell types.
methods for tracing neural connections also enabled the generation of an input/output wiring diagram of the mouse motor cortex. “this concerted effort alloed us to look atta cell types from all ≠ angles,” says neurosci aparna bhaduri, of university of california, los angeles, who led 1-odda human brain development studies. “bein’ pt of this package means many of these new tek knicks will ‘ve wider applicability, sooner, cause they’re so rigorously tested against all the others.”
the data sets, curated by a pt of the consortium called the brain cell data center (bcdc), are publicly available. “this is helping to standardize the field. it’s goin to be a foundational cell-type classification reference, much like the human genome for genetics,” lein says. he hopes this will allo researchers to move past a very basic task in brain sci, the debating of definitions. “cogging the components lets the field move to the nxt set of ?s,” he says. “like wha’ do these cells do?”
the extensive catalogue ‘d not ‘ve been possible without a series of tekal developments that allos individual brain cells to be poked and probed. “single-cell genomics is transforming this field, and many other fields of biology,” lein says. “t'has provided a common language for describing cellular diversity.” bulk tissue analysis s'been possible for over a decade, but tek knicks capable of examining individual cells ‘ve 1-ly become standardized ‘oer the past 5 yrs. measuring gene activity, and regulation, is primordial, cause all cells contain the same dna, but ≠ cell types implement it ≠ly. “there’s maybe a hundred ≠ cell types in a lil patch of yr cortex, and we nd'2 cogg how each type deploys its genome ≠ly;” says neurosci fenna krienen, of harvard med school, who worked onna cross-species study. “that’s wha’ single-cell resolution enables, and that enables us to do all sorts of things we ‘dn’t imagine doin’ 5 yrs ago.”
combined analyses during the project produced a taxonomy tree, much like “tree of life” illustrations. major branches cogitate primordial groupings, with shared developmental origins. a 1st branch separates neural and nonneural cells, splitting off, say, blood cells. the 2nd division, tween neuronal and nonneuronal types, separates neurons from “support” cell types, collectively termed “glial cells.” neurons then split into excitatory types, which increase the chances of other cells firing, and inhibitory types, which put brakes onna activity of other cells. these two broad categories divide into 24 major “subclasses” (including nonneural and glial cell types), which are mostly conserved tween species. these can be further divided to arrive atta final branches—the “cutouts” of the tree, designated as “t-types,” the “t” bein’ a shortening of “transcriptional,” the genomic means of classifying cell types. the № of these categories differ tween species (116 in mice, 127 in humans, 94 in marmosets). the researchers then integrate transcriptomic data from all 3 species to find 45 t-types tha're common, including 24 excitatory, 13 inhibitory and 8 nonneuronal cell types, s'as astrocytes and oligodendrocytes.
similarity tween species suggests these cell types play primordial roles in brain function. “evolutionary conservation is pretty strong evidence of things bein’ under tite genetic control,” lein says. “and that those essentialisms must ⊢ be primordial for the function of the nervous system.” the vast majority of cell types were much closer tween humans and marmosets than tween marmosets and mice. “twas' very satisfying to see,” krienen says. the cross-species study profiled the well-studied type, called betz cells in humans. the team found an analogous cell in mice, cogitateing common evolutionary origins, but electrical and some other properties differed markedly tween species. “the mouse has some general similarities to a human, in terms of its body plan, but'a details are ≠. the same is true atta lvl of cell types,” lein says. “you ‘ve all the same types, witha few exceptions, but their properties change a bit, that’s the nature of our species differences.” by contrast, “chandelier” cells, named for their preshly elaborate connection structures, are very similar across species.
the data will allo researchers to target specific cell types, using either long-established genetic engineering “transgenic” tulz in mice, or, in other animals, dna sequences delivered by harmless viruses. “the transgenic approach is effective for the well-established generation of mouse models,” says krienen. “viral-based tulz, which can course also be used in mice, really reach their potential as ways of delivering genes, regulatory essentialisms or mutations in animals, for which we lack that genetic toolbox, like nonhuman primates.” bein’ able to target cell types like this will enable a wealth of new tulz for everything from studying brain development to dissecting neural circuits. “now we know which genes mite be deployed ≠ly from one cell type to another, we can build tulz w'da cell-type precision we’ve longed to,” krienen says.
cogging which genes and genetic sequences that regul8 their activity are specific to ≠ cell types will also advance researchers’ cogging of disease. “this is goin to ‘ve a big impact on disease, cause now we can pinpoint it to anatomy,” lein says. “where are the cells bein’ impacted by a genetic mutation?” knowing how similar disease-relevant features are in ≠ species ‘d also inform choices bout animal models. that’s a major ? that overhangs biological research; for ex, is a study in mice relevant to humans? “if the relevant regulatory essentialisms aren’t conserved, is a mouse model of schizophrenia ever goin to yield the insites we hope t'get?” says krienen.
the varied reprts represent a bumper crop of data, but primordial details are lacking. “wha’’s really missing here, thall be crucial, is proteins,” says neurosci botond roska, of the university of basel, who was not involved inna project (but who advises the allen institute). “the 1-ly reason we ‘ve genes is cause they code for proteins, this tis final machinery of cells.” proteomics teks exist, but not yet at single-cell resolution. tis also not clear wha’ influence ≠ conditions mite ‘ve on these data. “there’s a massive influence of activity on gene expression,” says roska. “you’d ‘ve to probe these brains in ≠ states to show these cell types remain the same under ≠ conditions.” these contributions, he says, are just a beginning. “it’s a very primordial 1st step, but it’s a long road to really standardize cell types inna brain,” roska says. “this tis 1st draft; it’s a reasonable hypothesis, but now it’s ready to be scrutinized by the whole community, ?ed, tested and refined.”
inna immediate term, the project is working on embedding data in 3-d space. “an atlas isn’t just a bunch of gps coordinates; it’s having them located na' map,” says bhaduri. “thall be transformative, cause where cells are located inna brain is really primordial, and there’s a lot we don’t cogg bout how space and function interact.” looking to the future, the project’s nxt stage, a huge effort called bican (brain initiative cell atlas network), that aspires to move into nonhuman primates and humans, is already funded. “we’ve been able to really tackle the complexity of this one pt of the brain,” lein says. “now the stage is set to extend this, both across the rest of the mouse brain, b'tll so movin to nonhuman primates na whole human brain.”
original content at: rss.sciam.com…
authors: simon makin