PMC7664833_01
Development of the CLL in patient. (A-C) FACS of samples at diagnosis (A) relapse (B) and before the second therapeutic intervention (C). CLL fraction of cells in the sample is defined as fraction of CD5-positive CD23-positive cells which is supported by fraction of CD5-positive CD200-positive cells. (D) Progression of the numbers of leukocytes in the peripheral blood in time.
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This showed that virtually all CLL cells (CD200-, CD5-, and CD23-positive) were CD19-positive ( Figure 1A ). At this time point the second sample was collected (peripheral blood, termed relapse) and a CD19-negative sub-clone was detected by flow cytometry ( Figure 1B ). The disease showed an increased ratio of all CLL cells to CD19-positive cells to 5:1, suggesting further progression of the CD19-negative clone ( Figure 1C ). A timeline highlighting disease progression and sampling is shown in Fig 1D. This resulted into 7 clusters ( Figure S1A ), which were then assigned to known cell types using signatures from CIBERSORT (see  Supplementary Methods ). Three clusters, encompassing most of the cells, were classified as B cells, two clusters as T cells, and the two smallest clusters as NK cells and macrophages, respectively ( Figures 2C ,  S1A , and  S1B ). Visual inspection of the profiles revealed consistent differences between cells in the diagnosis- vs. relapse-enriched clusters ( Figure S1C ). This clone shows specific loss of chromosomes 3p and 17p ( Figures 2G  and  S1C ), confirming previous cytogenetic analysis at later collection. Using data from GenomicScape, we assigned each B cell to known stages of differentiation (see  Supplementary Methods ), then summarized the results by cluster ( Figure S1D ). IFT57) showing higher expression in the relapse-enriched clone that were significantly associated to poor prognosis in patients showing high expression (FDR <= 0.05). Interestingly, the gene that showed the highest significance was PARP14 ( Figure S1E ).
Single-cell transcriptomics highlights a shift in clones' dominance in advanced CLL. (A) Schematics of the approach. (B) Dimensionality reduction showing the captured single-cell transcriptomes (diagnosis and relapse). (C) Same as (B) but color-coded according to predicted cell types. (D) Dimensionality reduction applied to the cells in the B cell compartment from (C) (upper panel: color-coded by cluster, see Methods; lower panel: color-coded by diagnosis vs relapse). (E) Stacked bar charts indicating the relative cluster composition of the cells sampled either at diagnosis or relapse. (F) Stacked bar charts indicating both the absolute (left) and relative (right) composition of each cluster, in terms of cells sampled either at diagnosis or relapse. (G) Summary heat map showing the average CNA profile for each indicated subgroup (n = 6; for each one of the three clusters in (D), two groups were defined, one comprising the cells at diagnosis and another one those at relapse). Each column of the heat map represents a proxy for the number of copies for the region, estimated using a genomic windowing approach (chr1 to chr22), based solely on the scRNA-seq data. Bar charts on the right side of the heat map indicate the size of each group. Dendrogram indicate the results of hierarchical clustering performed using complete linkage and Ward.D distance.
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Figure 2A ), we obtained 1,062 and 2,026 cells, respectively ( Table S1 ). For further analyses we retained 845 and 1,623 cells from diagnosis and relapse passing filtering criteria, respectively. We then subject them to dimensionality reduction ( Figure 2B ) and graph-based clustering using Seurat (see  Supplementary Methods ). Three clusters, encompassing most of the cells, were classified as B cells, two clusters as T cells, and the two smallest clusters as NK cells and macrophages, respectively ( Figures 2C ,  S1A , and  S1B ). This resulted in three clusters ( Figure 2D ). While one of these clusters was dominated by cells from diagnosis (termed DIAG or diagnosis-enriched cluster), two of them were mainly composed by cells from relapse (REL1 and REL2; hereafter termed relapse-enriched clusters) ( Figures 2E, F ). Hierarchical clustering showed that the meta-profiles aggregate based on their malignancy status (the clusters defined in  Figure 2G ) rather than their origin (diagnosis or relapse). This clone shows specific loss of chromosomes 3p and 17p ( Figures 2G  and  S1C ), confirming previous cytogenetic analysis at later collection. Clustering of B cells data resulted in two main groups, with the one enriched for cells from the relapse sample divided into two sub clusters ( Figure 2D ).
Functionality of the differentially expressed genes. (A) Enrichment Map representation of the Reactome pathways significantly enriched in genes up-regulated in the relapse-enriched clone. Each node is a pathway. Size of the node is proportional with the number of genes associated to it. Nodes are connected based on similarity and represented as pies, indicating if DEGs annotated in the pathway that are either up- or down- regulated in the relapse-enriched clone. (B) UMAP visualization of B cells as shown in 
Figure 1D
, in which each cell has been color-coded according to the cumulative expression of the genes in the indicated gene set. (C) Bar charts showing the statistically significant enriched Reactome pathways in the lists of DEGs between the sub-clusters of the relapse-enriched cluster. (D) Box plots indicating the overall expression of a published signature predictive of time-to-progression in CLL for combined Rituximab, Fludarabine and Cyclophosphamide treatment, for each of the two main clusters identified. (E) Same as (C) but using the signature from (D).
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Table S3 ). To achieve a compact representation of the enriched terms, we used Enrichment Map ( Figure 3A ). I) and cytokine signaling ( Figures 3A, B  and  Tables S3,  S4 ). The latter has been previously associated to advanced disease. In contrast, down-regulated genes showed enrichment for signaling pathways related to cellular stress ( Figure 3A ). Of note, these analyses also suggested a metabolic difference between the diagnosis-enriched and relapse-enriched clones, as hinted by enrichment in glycolytic pathways in the former ( Figure 3B ). Table S5 ). Reactome enrichment analysis revealed that these DEGs are significantly enriched in signaling pathways that are related to different aspects of B cells functionality like CD28, ZAP70, or PD-1 signaling ( Figure 3C ), and in pathways already identified when comparing the diagnosis-enriched to the entire relapse-enriched cluster ( Figure 3B ,  Tables S3 and S4 ). This suggests further heterogeneity in the expression of these pathways at relapse. This suggests that cells in this clone might have a more quiescent phenotype than the dominant clone before treatment, which is in line with the functional enrichment of the DEGs ( Figures 3A-C ). Those cells in the relapse-enriched clone (particularly those in REL2) showed indeed higher levels of expression for the genes in this signature, compared to the cells in the diagnosis-enriched clone (p-value = 2.1e-12; Wilcoxon rank-sum test;  Figures 3D, E ).