Allen Cell Types Database
This is the online help for the Allen Cell Types Database web application.
The Dataset
The Allen Cell Types Database contains a multimodal characterization of single brain cells by electrophysiology and morphology, as well as single-cell transcriptional data from nuclei or whole cells. These resources enable data-driven approaches to classification and are integrated with other Allen Brain Atlas applications. Data were collected from adult transgenic mice from fluorescent Cre-positive cells or Cre-negative cells, or from adult human neocortical cells.
The Allen Cell Types Database currently includes the following cell characterization datasets for mouse and human cells
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Electrophysiology: Whole-cell current clamp recordings made from identified, fluorescent Cre-positive neurons or nearby Cre-negative neurons in acute brain slices derived from adult mice. Whole-cell current clamp recordings made from adult human neocortical neurons in brain slices derived from surgical specimens.
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Morphology: reconstruction-quality, 2D image stacks containing the complete structure of mouse and human neurons filled and recorded from in vitro slice preparations and 3D reconstructions of the dendrites and the initial (spiny neurons) or complete axon (aspiny neurons) of each neuron.
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Histology: Images of immunohistochemical staining from human brain slices using antibodies chosen to evaluate tissue integrity, cell distribution and histopathology. The panel includes Nissl, NeuN, SMI-32, GFAP, PVALB, Iba1 and Ki67.
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Transcriptomics: single-cell RNA-sequencing with SMART-Seq v4 on fluorescent Cre-positive and Cre-negative cells enriched by FACS for mouse and fluorescent NeuN-positive (neuronal) or NeuN-negative (non-neuronal) nuclei enriched by FACS for human.
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Generalized leaky integrate-and-fire (GLIF) models: a series of point neuron models of increasing complexity to reproduce the spiking behaviors of the recorded neurons. Starting with a leaky integrate-and-fire model, more complex models attempt to model variable spike threshold, afterspike currents, and threshold adaptation.
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Biophysical - perisomatic models: compartmental model of neurons that account for the neural morphology and emulate electrophysiological responses by assuming biophysically detailed mechanisms for specific families of ionic conductances, with passive dendrites and active conductances at the soma.
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Biophysical - all active models: compartmental model of neurons that account for the neural morphology and emulate electrophysiological responses by assuming biophysically detailed mechanisms for specific families of ionic conductances, with active conductances throughout the cell.
Key features
• Registration to the mouse CCF, a 3-D anatomical framework with 3-D structural annotations.
• Data traces for the electrophysiology data from each neuron.
• Manually corrected and curated dendritic and axonal morphologies with annotation of neuronal compartments and extracted quantitative values for each reconstruction.
• RNA sequencing data derived from the LGd, VIS, and MOs for mouse and from the MTG for human.
• Search and visualization tools for exploring the single cell transcriptomics for LGd, and single cell electrophysiology, morphology, and modeling data.
• Data and models available for download via Cell Types Database API and Allen Software Development Kit (SDK)
For complete details please see the white papers on our Documentation page.
Greetings, ABM community
with reference tot his announcement,
What makes us human? Detailed cellular maps of the entire human brain reveal clues - Allen Institute I cannot figure out where to find the list of 3000 brain cell types in the mammalian brain, nor how to extract them. The main question for me is to find out a) their identifier for each cell type (are they identified by name? by a code?) and the attributes for each, Kindly point me, thank you
The GitHub repository associated with the Science paper, “Transcriptomic diversity of cell types across the adult human brain” includes information about all 3,313 subclusters defined therein. Supplemental Table 3 in the paper itself includes more extensive annotation of the 461 clusters (each cluster includes every cell from one or more subclusters) including identifiers, marker genes, brain regions of dissection, neurotransmitters, etc., and does not required coding to parse. A corresponding table for the subclusters may be available; if this would be useful please reply here and I will see whether such a table could be shared here.
Jeremy, thank you, I found an open access version of the paper, but again the figures are illegible https://www.researchgate.net/publication/364533640_Transcriptomic_diversity_of_cell_types_across_the_adult_human_brain and this web page associated with the repo you point to that looks the nearest thing Cellxgene Data Portal, *all neurons but when I try to download the dataset it says 30 gb, can this be? this is not reasonable for me- a simple spreadsheet or text file with, say 300 lines per page, over 13 pages should be easier to download
It would be awesome if you could help ot produce such a resource and post it here, so finally I can take a look at those 3000+ cell types that I have been longing to know about for such a long time, Ideally the table could contain for each row a link to a more detailed view of each cell type, or cluster then I ll start asking questions I am sure I have about how they are modelled or how to use the data etc. Let me know if I can assist in producing the table thank you very much!!!
Here is the requested table of subcluster-level metadata, as provided from the lead author of the study, Kimberly Siletti: subcluster_annotation.csv (1.3 MB). Additional information related to this data set is forthcoming in the next couple of months, so stay tuned.
This is SO AWESOME thank you, finally something that I can read 
does this data has a license? (can I share it? ) Okay, I opened the csv as a a google spreadsheet, and can see 3312 rows, which I understand correspond approx to the number of cell types mentioned in the article that brough me here. Great. To start with, I am looking for the following answers (I am ready to go through the literature, but maybe a quick walk through? hoping that I do not come across as toooo demanding ) 1, the article introduces this work as cell types, but in the data set they are called subclusters, it could be helpful to have some understanding of the naming conventions 2. I can see the cluster and supercluster associations, which gives me a sense of hiearchycal class structure, is there a visual diagram.mindmap .list of the clusters and superclusters (how many clusters/superclusters there, what are their naems and what do they represent) 3. an overview of the column names, what do they stand for? what are these data in the columns used for, what kind of analysis can be done, Once I have this understanding, it will be easier for me to work with the tools. Ideally, when we have answers for 3. it may be easier for us to figure out which tools/utilities to use to perform which analysis. I d love to see the super/sub/clustrs in a mindmap format, do you think it is feasible? please do not hesitate to let me know how can I help this exploration, and the production of the neuroscience for dummies resources, compatibly with my other deadines. thanks again Jeremy and all
If you use this table, please be sure to appropriately cite the Science paper, “Transcriptomic diversity of cell types across the adult human brain .” I’m not sure what the official rule is for resharing, but it’s probably safe to treat it as if it were a Supplemental Table in a paper and proceed accordingly. As for all of your other questions, these go well beyond the scope of a single Community Forum post (and include several active topics of discussion at the Allen Institute and BICAN!). I will say that both the paper and the GitHub repo go some of the way in addressing these questions, and additional information is forthcoming from the Allen Institute in a couple of months. Sorry I can’t be of more help on this right now!
Regarding “neuroscience for dummies resources”, while I’d phrase it differently, the Allen Institute has a variety of Education and STEM Resources aimed at more general audiences and classrooms, and we’re also creating a Cell Type Taxonomies A-Z: Webinar Series (of which two are complete and on YouTube) focused on brain cell types and taxonomies, and how and why to use these resources in your research. You and our peers may find such resources useful.
Jeremy, thank you. I have started analizying the data in the summary table provided by Kim which you repost, now can I finally make sense of it from a human intelligence viewpoint, based on a comparative analysis of the datasets that you point to here, CORRECTION: I have found a bunch of superclusters which are not labelled in the pie chart (from email) so they may appear to be missing , /CORRECTION ENDS
Choroid plexus
Ependymal
oligodendrocyte precursor
Hippocampal CA4
Vascular
Fibroblast
Bergmann glia
Hi all,
first of, thank you so much for putting together this data base and sharing it openly! I’m interested in the human pyramidal L2 and L3 cells. I would like to use the electrophysiological measures to constrain my biophysically detailed model of the human neocortex that I’m currently. I tried to download the data from dandi (DANDI Archive) and load them into Python using pynwb, but if I’m not mistaken, those data only contain spike information in the ProcessingModule. I also tried to download a specimen using the allensdk, and receive this error: HTTPError: HTTP Error 503: Service Unavailable
Finally, I have manually downloaded a nwb.file from the data explorer here (571735022_ephys.nwb) using pynwb * and got this error: TypeError: Missing NWB version in file. The file is not a valid NWB file.
Any hints on how I could get my hands on these data? I’d love to tune my model based on the electrophysiological properties reported in Berg et al, 2021.
Apologies in advance if I’m missing something super obvious. Any help would be appreciated! Thanks!
import pynwb
nwb_file_asset = pynwb.NWBHDF5IO(‘571735022_ephys.nwb’, mode=‘r’, load_namespaces=True)
nwb_file = nwb_file_asset.read()