(91a) Graphical Model Framework for Automated Annotation of Cell Identities in Dense Cellular Images.

C. elegans is an extremely useful model organism in understanding neural basis of behaviors because of several advantageous features. The nematode has a small and fully mapped nervous system, displays complex behaviors, and is amenable to fluorescence imaging. Recent advances in microscopy have enabled simultaneous recording of activities of the entire brain of freely behaving C. elegans (whole-brain imaging) thus producing a mine of rich datasets. However, in order to compare recorded neuron activities across animals and experimental conditions, biological names of neurons must be identified in videos. Each neuron of C. elegans has been assigned a biological name. Further, huge body of literature describing individual neuron properties is available for several neurons. Incorporating this literature with whole-brain imaging experiments is not possible without resolving cell identities.

Although some methods exist, they were either not designed for handling dense images [1-2], and thus have lower accuracy requirements, or use registration methods [3-4], which perform poorly in handling noises common in data. The lack of an accurate automated method is becoming increasingly the bottleneck in analyzing large number of datasets in multi-cell and whole-brain functional imaging [5-6].

To address these challenges, we present a probabilistic graphical model framework, CRF_Cell_ID [7], orthogonal to previous registration methods. The algorithm based on Conditional Random Fields [8] annotates cell identities by maximizing intrinsic similarity between shapes (similar to domain adaptation for matching distributions in machine learning). We quantitatively show our method achieves higher accuracy and is more robust against two types of noises prominent in data (position variability and missing cells) compared to popular methods. CRF_Cell_ID boosts accuracy by building new data-driven atlases in highly computationally efficient manner compared to previous methods. Further, data-driven atlases can be built using partially annotated datasets or datasets from strains with non-overlapping cells, a task not possible by previous registration based methods.

We demonstrate wide applications by identifying cells in gene expression localization, multi-cell calcium imaging, and whole-brain imaging in C. elegans. We also demonstrate generalizability across strains and imaging conditions by 1) extending framework to incorporate new information in different strains such as landmark cells or chromatic code [4] and 2) showing application in handling images with different animal orientations. We demonstrate the power of our computational framework in a real use-case of whole-brain imaging, where automatic annotation enabled us to identify two distinct groups of specific cells in recordings: encoding responses to food and controlling spontaneous locomotion.

Our framework will enable fast and unbiased cell identification in whole-brain videos thus generating big annotated datasets. This will enable application of newer computational techniques to be applied to data (earlier limited by lack of cell identities) such as Tensor Component Analysis [9] or deep learning based methods and will enable understanding of how global brain dynamics generate and coordinate behavior.

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2. Long, F., Peng, H., Liu, X., Kim, S. K., & Myers, E. (2009). A 3D digital atlas of C. elegans and its application to single-cell analyses. Nature Methods, 6(9), 667–672. https://doi.org/10.1038/nmeth.1366
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6. Kato, S., Kaplan, H. S., Schrödel, T., Skora, S., Lindsay, T. H., Yemini, E., Lockery, S., & Zimmer, M. (2015). Global Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans. Cell, 163(3), 656–669. https://doi.org/10.1016/j.cell.2015.09.034
7. Chaudhary, S., Lee, S. A., Li, Y., Patel, D. S., & Lu, H. (2021). Graphical-model framework for automated annotation of cell identities in dense cellular images. ELife, 10, e60321. https://doi.org/10.7554/eLife.60321
8. Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. ICML ’01 Proceedings of the Eighteenth International Conference on Machine Learning, 8(June), 282–289. https://doi.org/10.1038/nprot.2006.61
9. Williams, A. H., Kim, T. H., Wang, F., Vyas, S., Ryu, S. I., Shenoy, K. V., Schnitzer, M., Kolda, T. G., & Ganguli, S. (2018). Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis. Neuron, 98(6), 1099-1115.e8. https://doi.org/10.1016/j.neuron.2018.05.015