Fig 1.
Description of the UPSIDE workflow.
(A) Single cells are segmented directly from brightfield images and deep learning UNET architecture to predict synthetic fluorescent images [23]. Segmented cells are then pre-processed to generate separate mask and texture images, which are then used to concurrently train two variational autoencoders (VAEs). The shape and texture encodings learnt by these two VAEs are then concatenated and used for downstream data analysis. (B) Encoded latent vectors are then decoded into a shape and texture image to aid the interpretation of the encoded features.
Fig 2.
UPSIDE distinguishes morphologically-distinct blood cell types in a heterogeneous population.
(A) Images of four different blood cell types were mixed together and passed through the UPSIDE workflow. Resultant shape and texture images were used to train concurrent VAEs. Output latent encodings were weighted relative to each other, concatenated, then projected onto a 2D plane using UMAP. (B) Dot plots show distribution of each cell type projected on 2D UMAP space made by UPSIDE. (C) 2D UMAP projection of the VAE-generated encodings that have been grouped into different morphological clusters using Louvain clustering algorithm. Representative brightfield cell crop images from the different clusters were listed. Scale bar represents 5 μm. (D) Cell type fractional composition within each cluster. A fixed number of cells from each cell type were sampled, and the cluster-wised cell type composition was calculated from this pooled population.
Fig 3.
Cell-type specific morphological features can be interpreted by decoding the latent space cell representation.
(A) Clustergram of average z-scores for latent shape and texture features for different cell clusters (see Methods section for how z-scores values were calculated). (B) Five mask features with highest z-scores for each morphological cluster are decoded and visualized. Clustergram shows matrix of correlation coefficients for forty mask features having the highest standard variation in the dataset. Scale bar represents 5 μm. (C) Individual texture features were clustered into eight groups (T1-T8) according to their correlation with each other from the clustergram of texture features. Each group was decoded into brightfield difference images for interpretation (see Methods). Scale bar represents 5 μm.
Fig 4.
UPSIDE identifies stem cell-associated morphological states from patient-derived AML leukemic cells.
(A) LSCs (CD34+CD38-) from an acute myeloid leukemia patient were cultured in cytokines with or without AhR inhibitors (UM729 and StemRegnin1) filmed for ~5 days (left). Brightfield images were captured once every 3-5 minutes. αCD34-APC and αCD38-PE antibodies were added in situ, and fluorescent images were captured once every hour (top right). Still frames show representative time lapse images of AML cells (bottom right). Scale bar represents 10 μm. (B) UMAP 2D projection of the UPSIDE generated latent space cell representations. Individual morphological clusters were identified using the Louvain Clustering algorithm, then grouped manually based on their proximity to each other in the latent space (See S6B Fig). Representative cell images from each cluster were also shown. Scale bar represents 10 μm. (C) Clustergram shows Z-scores of the latent mask and texture encodings for each morphological cluster. (D) Decoded images of the four most enriched features for each morphological state. Texture features were visualized using difference maps that were zoomed in around the decoded cells. (E) Area, eccentricity, and edge strength for each cell were calculated and mapped to the UMAP latent space representation. (F) CD34 and CD38 levels were mapped onto the UMAP. (G) Violin plots show distributions of CD34 and CD38 expression levels in different morphological clusters (left). (H) Histograms showing log CD34 levels against CD38 levels for each morphological cluster (right).
Fig 5.
Population dynamics of identified morphological states.
(A) Population fraction dynamics over time for each morphological cluster with (right) or without (left) AhR inhibitors (top). Population fraction contribution of each cluster at the last time point of the culture (bottom). Comparisons of the population fraction with and without AhR inhibitor were performed using the Chi-Square test for the dependency between the AhRi treatment and a cell’s cluster identity. **: p < 0.001, *: p <0.05 (B) UMAP showing CD34 and CD38 expression levels at different time points, in the presence or absence of AhRi.
Fig 6.
Calculation of morphological state transition probabilities by cell linkage analysis.
(A) Cell pairs found in proximity across on successive time points were linked (left). Cell linkages, along with assigned morphological states of linked cells, were used to calculate transition probabilities between all states. (B) Heatmap shows transition probability matrix between all morphological clusters (left); image montage shows representative cell tracks identified from the culture (right). Scale bar represents 10 μm. (C) Distribution of cellular velocity for linked cells for each morphological cluster. (D) Plot shows mean cell velocity against mean cell eccentricity for each morphological cluster.