Abstract A013: Integrating imaging and sequencing to compute the subcellular organization of a cell’s transcriptome

Abstract

Abstract Background: Incorporation of prior information in the form of pathway activity profiles was key in the success of the algorithm that won the DREAM challenge to predict in vitro cell fitness from transcriptomic and other multi-omic datasets (Costello, C. C et al, 2014). We hypothesize that leveraging additional prior information on the spatial distribution of transcriptome activity inside the cell will yield better predictions of cell fitness, which span longer timeframes. Methods: We integrated (i) sequencing and (ii) imaging data obtained from a stomach cancer cell line (NCI-N87). For (i), we used previously published scRNA-seq data available for 3,246 NCI-N87 cells. Cells were assigned to either G0/G1, S or G2M phase; and G0G1 cells were grouped into subpopulations defined by somatic copy number alterations. In addition, we calculated the pathway activity profile of each cell using gene set variation analysis. For (ii), cells were imaged on a Leica confocal SP8 using 63X objective, collecting 70 z slices of target dye and brightfield with interslice interval 0.29 µm. We trained a previously developed label-free U-Net convolutional neural network (CNN) (Ounkomol, C. et al, 2018) on Z-stacks of images containing the nuclei or mitochondria (mito) to calculate the spatial distribution of the two organelles. Models were trained for nucleus using train (N=37)/test (N=5) and mito using train (N=24)/test (N=5) with the Adam optimizer for 150,000 minibatch iterations monitoring the weighted mean squared error (MSE). The model training pipeline was implemented in PyTorch on a Nvidia DGX A100 Tesla V100 GPU. The accuracy of the model was assessed by calculating the Pearson correlation coefficient between the pixel intensities of the model’s predicted output and the independent test images. The predicted 3D organelles were used as input for segmentation using the Cellpose algorithm (String, C. et al, 2021), giving us nucleus and mito coordinates (X,Y,Z) for each cell. To integrate (i) and (ii) we overlaid the distributions of nucleus and mito area and volume onto the activity of pathways expressed in the nucleus, mito, and their respective membranes. Sequenced and imaged NCI-N87 cells were then co-clustered together to obtain a tree that links profiles between the two assays. Results: Overall, the correlation coefficient (r) was higher when using nucleus images for training (r=[0.759, 0.833]; average 0.780) compared to mito (r=[0.633 - 0.783]; average 0.680), even when the sample sizes were equivalent. Of the imaged cells detected within a given field of view, 50-80% were linked to a sequenced cell. Linking sequenced and imaged cells allows visualizing the spatial distribution of pathway activity among various organelles inside a cell. Conclusions: While our results demonstrate how this can be achieved in principle computationally, they will require extensive experimental validation. Doing so will transform omics-based predictions of cell fitness into problems that can be solved by image classification algorithms and recent advances in computer vision. Citation Format: Andrew R. Schultz, Saeed Alahmari, Pallavi Singh, Zaid Siddiqui, Emily Thomas, Emek Demir, Laura Heiser, Noemi Andor. Integrating imaging and sequencing to compute the subcellular organization of a cell’s transcriptome [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A013.