Functional Subsets of Plasma Cells Associated with Amyloid Production and Venetoclax Sensitivity in Light Chain Amyloidosis

doi:10.21203/rs.3.rs-1609283/v1

Download PDF

Article

Functional Subsets of Plasma Cells Associated with Amyloid Production and Venetoclax Sensitivity in Light Chain Amyloidosis

https://doi.org/10.21203/rs.3.rs-1609283/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Light-chain amyloidosis (AL) is characterised by a systematic deposition of amyloidogenic immunoglobulin light chains produced by pathogenic bone marrow plasma cells (BMPCs), the features of which remain elusive. Herein, we conducted a single-cell RNA-seq and image flow cytometry analysis of BMPCs in AL in comparison with those in healthy controls and their precursors: monoclonal gammopathy of undetermined significance (MGUS). We identified seven BMPC subsets with overlapping but distinct functions such as DNA repair, cell proliferation, osteoclast differentiation, chromatin organisation, and immunoglobulin production. The subsets enriched in AL overexpressed amyloid-beta binding protein-encoding genes, such as apolipoprotein E (APOE) and cystatin 3 (CST3), and exhibited plasmablastic morphology. The subsets related to aberrant light-chain production in AL upregulated pathways of neutrophil degranulation and transportation to as well as modifications in the Golgi apparatus, especially that of N-linked protein glycosylation. The predominant subset was most susceptible to glucocorticoid and proteasome inhibitors and highly expressed venetoclax sensitivity-associated genes in t(11;14) AL. We further validated a patient subgroup featuring a larger proportion of the predominant subset and a higher expression of cyclin D1 (CCND1) and venetoclax sensitivity-associated genes. Our findings provide insights into the mechanisms underlying the functional heterogeneity of BMPCs in AL.

Light-chain amyloidosis (AL), the most prevalent form of amyloidosis, is an uncommon plasma cell dyscrasia. In AL, misfolded immunoglobulin light-chain proteins are produced by pathogenic bone marrow plasma cells (BMPCs) and deposited in multiple organs, resulting in irreversible organ damage manifesting as nephrotic syndrome, restrictive cardiomyopathy, and peripheral neuropathy^{1, 2}.

In the early stages of AL, monoclonal gammopathy of undetermined significance (MGUS) progresses to a spectrum of plasma cell dyscrasias characterised by plasma cell proliferation and monoclonal protein production³. Multiple myeloma (MM) is a prototype of plasma cell dyscrasia characterised by malignant plasma cell proliferation. Although cell proliferation also occurs in AL, aberrant light-chain production and organ deposition are also observed. To date, plasma cell proliferation has gained more attention in research than light-chain production; therefore, AL therapies often follow the treatment for MM⁴.

Thus, these two distinct dysfunctions that contribute to AL progression and development reveal the functional heterogeneity in the BMPCs that participate in AL pathogenesis, although different subset populations need to be characterised within the pathogenic BMPCs of AL. Previous studies on BMPCs in AL at the bulk level revealed little or no differences between different functional subsets, even between pathogenic and relatively normal BMPCs. This poor resolution was largely attributable to the small proportion of pathogenic BMPCs present in AL as compared to that in MM, resulting in an overwhelming signal from normal BMPCs in the bulk transcriptome⁵.

Identification of functional subsets can reveal intra-individual BMPCs heterogeneity; their variation determines the inter-individual heterogeneity among AL patients, including chemotherapy responses^6–8 and organ deposition tropism of misfolded light chain⁹, which are critical to disease management. For example, t(11;14) (IgH/CCND1) translocation is associated with adverse outcomes in AL patients treated with bortezomib⁶, but favourable outcomes when treated with venetoclax^{7, 8}. Thus, clear characterisation of the molecular subtypes in AL patients would facilitate more effective personalised interventions.

Previous single-cell RNA sequencing (scRNA-seq) studies have investigated the role of different BMPC subpopulations in plasma cell dyscrasias¹⁰; however, these remain poorly described in AL. One study exploring the single-cell landscapes of MGUS, MM, smouldering MM, and AL demonstrated an overlap in the upregulated genes shared between AL and other plasma cell dyscrasias, although it lacked insights into the underlying mechanisms¹¹. A recent study comparing the transcriptional programs in AL, MM, and MGUS revealed that BMPCs in AL resembled plasma cells in secondary lymphoid organs, which upregulated protein N-linked glycosylation–related transcriptional programs. However, most of these observations were obtained from the bulk transcriptome ¹².

Therefore, we conducted a single-cell RNA-seq on clinical samples from AL patients, MGUS patients, and healthy controls to better understand the nature of single BMPCs in AL (Table S1). To this end, we designed an analytical pipeline to explore the inter- and intra-individual heterogeneity of BMPCs in AL, including t(11;14) and non-t(11;14). In particular, we identified functional subsets of BMPCs and focused on characterising those associated with amyloid light-chain production and venetoclax sensitivity. We also validated these novel findings regarding BMPC subsets through image flow cytometry at the protein level and an independent cohort of bulk RNA-seq of BMPCs in AL.

Patient recruitment and sample preparation

This study was approved by the Ethics Committee of Peking Union Medical Hospital, Beijing, China (approval number: JS-3455). Bone marrow aspiration samples were donated by one healthy control, two MGUS patients, and eight AL patients, who provided informed consent. Patient and healthy donor sample information is listed in Table S1.

We diluted 9 mL bone marrow aspirate with 9 mL PBS, then separated the lymphocytes using the lymphocyte separation medium and SepMate (STEMCELL Technologies, 86450), following the manufacturer’s instructions. We used magnetic beads conjugated to anti-CD138 antibodies (Miltenyi Biotec, 130-051-301), MS columns (Miltenyi Biotec, 130-042-201), and MiniMACS Separator (Miltenyi Biotec, 130-042-102) to enrich BMPCs. The quality control standards of the BMPC suspension included cell viability > 85%, a concentration of 700–1200 cells/μL, and a cell diameter of 5–10 μm.

Immunofluorescent staining and image flow cytometry

The BMPCs in suspension were stained with antibodies, including mouse anti-Syndecan-1 (Abcam, ab181789), rabbit anti-B4GALT1 (Abcam, ab121326), rabbit anti-APOE (Abcam, ab183597), rabbit anti-STMN1 (Abcam, ab52630), pre-adsorbed goat anti-rabbit secondary antibody (Alexa Fluor® 647) (Abcam, ab150083), and pre-adsorbed goat anti-mouse secondary antibody (Alexa Fluor® 488) (Abcam, ab150117), following the protocol of the eBioscience™ Foxp3/Transcription Factor Staining Buffer set (Thermo Fisher Scientific, 00-5523). The immunofluorescence-labelled BMPCs were analysed using ImageStreamX Mark II (Millipore), and the data were analysed using IDEAS (Version 6.2).

10X genomic single-cell RNA sequencing (scRNA-seq)

BMPCs in suspension were processed for scRNA-seq following the 10X Genomics 3' Chromium v2 protocol. The library was sequenced with ChromiumTM Controller (10X Genomics), converted into sequenced reads by CASAVA base recognition, and stored in FASTQ format.

scRNA-seq data pre-processing

We used fastp to perform quality control and trimming of the raw RNA sequencing data and then to align the sequence reads to the GRCh38 (hg38) reference genome. In total, 22 440 cells were successfully demultiplexed after pre-processing and filtering using CellRanger: normal PC (n=2 407), MGUSPC1 (n=4 280), MGUSPC2 (n=2 847), pALPC1 (n=4 328), pALPC2 (n=2 747), and pALPC3 (n=5 831). We used Seurat to process the single-cell data matrix (Supplementary Methods).

BMPCs of healthy controls from the public scRNA-seq dataset

We collected BMPCs of healthy individuals from the public scRNA-seq dataset (GEO accession: GSE120221)¹³ as healthy controls. Harmony¹⁴ was used to integrate 53 735 bone marrow single-cell transcriptome data of 20 healthy individuals. We designed a pipeline to identify BMPCs in the bone marrow single-cell transcriptome using DoubletFinder¹⁵ and SingleR¹⁶ according to cell markers (Table S2). A total of 717 BMPCs were isolated from 20 healthy individuals and marked as publicPC (Supplementary Methods).

Identification of functional subsets of single BMPC transcriptome

We used the “FindIntegrationAnchors” function wrapped in the Seurat R package to integrate the samples based on the canonical correlation analysis algorithm. All BMPCs were clustered into seven clusters at a resolution of 0.4 by the Louvain algorithm.

Functional analysis of single-cell transcriptome using algorithms

Slingshot¹⁷ and LandSCENT¹⁸ were used to analyse the trajectories involving the seven functional subset and calculate the signalling entropy.GSEABase, GSVA, enrichplot, and clusterProfiler were used in the gene set enrichment analysis (GSEA) based on the “Amyloidosis” gene sets in the DisGeNET database. Metascape (http://metascape.org/) was used for functional enrichment. InferCNV¹⁹ was used to estimate copy number variation (CNV) based on a single-cell data matrix. SCENIC²⁰ was used to infer regulons and transcription factors from the single-cell data matrix. STRING (https://string-db.org/) was used to infer protein–protein interactions (PPI) among the differentially expressed genes. Cytoscape was used to visualise the PPI network (Supplementary Methods).

Bulk RNA-seq of BMPCs in AL from public dataset

We collected the bulk RNA-seq dataset from the AL cohort (GEO accession: GSE175384)¹². Twenty-nine AL samples were included after removing three as outliers based on the PCA results.CIBERSORT was used to impute the functional subset from bulk RNA-seq based on the single-cell analysis results. The expression data of differentially expressed genes for the seven BMPC subsets were used as the signature gene files.

Statistical analysis and visualisation

R (version 4.0.3) was used to conduct statistical analysis and data visualisation. We used the pheatmap, ggplot2, ggpubr, and EnhancedVolcano packages in R.

Single-cell transcriptome revealed seven topologically continuous BMPC subsets in AL, MGUS, and healthy controls

To demonstrate differences in the pathological progression of BMPCs between AL and MGUS, we first performed a single-cell transcriptomic analysis using 14 081 BMPCs collected from bone marrow samples donated by AL patients (n=3), MGUS patients (n=2), and healthy controls (n=1+20). The doublets were then removed (Fig. S1a) and single cells were clustered (Fig. S1b) based on the differentially expressed genes (Fig. S1c), immunoglobulin production (Fig. S1d) and marker gene expression (Fig. S1e, Table S2) facilitated by the machine-learning algorithm (Fig. S1f).

To identify subsets of BMPCs that were specifically associated with AL or MGUS, we conducted UMAP (Fig. 1a) and three-dimensional (3D) UMAP (Fig. 1b) embedding of the integrated transcriptome of BMPCs (Fig. S2a), revealing the topological continuity of the seven subsets (Fig. S2b) in the AL, MGUS, and healthy control samples. This showed differences in proportions and gene expression.

Examination of BMPC marker genes showed differences in expression among the subsets. The majority of BMPCs were syndecan 1 (SDC1, encoding CD138) (+), CD38 (+), positive regulatory domain I-binding factor 1 (PRDM1) (+), X-box-binding protein 1 (XBP1) (+), interferon regulatory factor 4 (IRF4) (+), CD19 (−), and membrane spanning 4-domains A1 (MS4A1, encoding CD20) (−) (Fig. 1c). Markers for long-lived plasma cells, such as Thioredoxin Interacting Protein (TXNIP) and C-X-C motif chemokine receptor 4 (CXCR4), were expressed in all BMPC subsets except for subset 2.

The predominant subset (subset 0) was more abundant in AL and MGUS. In contrast, subsets 2–4 were more abundant in healthy controls (Fig 1d), and subsets 5 and 6 were specifically enriched in AL and MGUS, respectively (Fig 1e). These differential proportions of subsets among AL, MGUS, and healthy controls reveal that changes in subset proportions were likely associated with the progression towards MGUS and then to AL.

Differentially expressed gene expression further showed that subsets 0 and 1 shared greater transcriptomic similarity with each other than with other subsets, while subsets 2–6 had specific characteristics (Fig. 1f). The AP-1 complex, including jun proto-oncogene (JUN) and fos proto-oncogene (FOS), was upregulated in subsets 0 and 1. Beta-1,4-galactosyltransferase 1 (B4GALT1), a protein glycosylation enzyme localised in the Golgi apparatus, was expressed in subsets 0 and 1. Amyloidosis-associated genes such as apolipoprotein E (APOE) were distinctly expressed in subset 5, and were enriched in AL. Stathmin 1 (STMN1), involved in DNA repair and microtubule disassembly, was specifically expressed in subset 6. CD27 was highly expressed in subsets 3 and 4, which were enriched in the healthy controls. The functional subsets marked by APOE, STMN1, and B4GALT1 expression (Fig. 1f) were validated using flow cytometry. Subset 5, highly expressing APOE, and subset 6, marked by STMN1, largely contained plasmablastic BMPCs (Fig. 1g, S3a). The proportions of subsets 5 and 6, revealed by scRNA-seq (Fig. 1d) and image flow cytometry (Fig. 1g, S3a), were similar. Plasmablastic BMPCs showed higher roundness, higher nuclear–cytoplasmic (NC) ratio, and lower nuclear eccentricity compared with mature BMPCs. The subcellular distribution of APOE (in vesicles), STMN1 (in the cytosol), and B4GALT1 (in the Golgi apparatus) was consistent with previous findings (Fig. 1h, S3b).

As the basis of subsequent analysis, these transcriptomic subsets, defined by differences in their gene expression and abundance in AL, MGUS, and healthy subjects, revealed the intra-individual heterogeneity of BMPCs.

Functional subsets with gradients of signalling entropy and immunoglobulin production were related to AL pathogenesis

Based on these results, we sought to determine the functional implications of these transcriptomic subsets. Interestingly, the signalling entropy (Fig. 2a) and immunoglobulin production (Fig. 2b) showed an inversely proportional relationship and formed a gradient across distinct BMPC subsets. Subset 2 exhibited the highest expression of immunoglobulins and the lowest signalling entropy, whereas subsets 0 and 6 showed the inverse. Because signalling entropy is interpreted as the capacity for differentiation of individual cells, we inferred that subset 6 was less differentiated, whereas more differentiated BMPCs showed higher immunoglobulin production.

Pseudo-time analysis revealed three distinct trajectories among these BMPC subsets, following a gradient of decreasing signalling entropy and increasing immunoglobulin production (Fig. 2c). Subset 6 was the root of all three trajectories, and the predominant subset was adjacent downstream to subset 6. The other subsets were located further downstream of the trajectory.

We then conducted functional enrichment analysis to explore the relationship between trajectories and the roles of BMPC subsets in AL pathogenesis. The results indicated that subset 6 was involved in the cell cycle, DNA repair, DNA replication, chromatin conformation change, and upregulation of chaperone-mediated unfolded protein response (UPR), which supported the role of this subset as the root. The predominant subset, downstream of subset 6 in the trajectory, showed upregulation of oxidative stress, endoplasmic reticulum stress (ER stress), and osteoclast differentiation. This subset was also more responsive to glucocorticoid and proteasome inhibitors such as bortezomib. The predominant subset then underwent a trajectory into subset 5 or subset 1 in a pseudo-time. Subset 5 was enriched for the upregulation of neutrophil degranulation pathways and supramolecular fibre organisation, while subset 1 was enriched for carbohydrate biosynthesis and chromatin organisation. In addition, subsets 2–4 showed up-regulated immunoglobulin production and Fc-gamma receptor signalling (Fig. 2d).

Analysis of subset-specific transcription factors further supported the functional roles of different subsets. In particular, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin subfamily C member 2 (SMARCC2), a regulator of DNA helicase gene expression, dominated subset 6. AP-1 family members were predominantly expressed in the 0. X-ray repair cross complementing 1 (XRCC4), a known controller of V(D)J recombination, regulated gene expression in subset 5. DNA damage inducible transcript 3 (DDIT3) and nuclear factor I C (NFIC), which are activated by ER stress, were expressed in subset 2 (Fig. 2e, S4a).

Taken together, these results depict the functional roles of BMPCs in AL pathogenesis, associated with an inverse relationship between immunoglobulin production and signalling entropy.

Immunoglobulin idiotype, marker gene expression, and copy number variations contributed to the inter-individual heterogeneity of BMPCs in AL

To explore intra-individual and inter-individual heterogeneity, we conducted UMAP (Fig. 3a, 3b) and 3D UMAP (Fig. 3c) embedding with the merged transcriptome of BMPCs. The plots obtained revealed a flower-like topology with the healthy controls relatively tightly clustered at the centre, while the AL and MGUS samples projected out, like petals, with no clear relationship with each other except connections through healthy controls. These results suggest that BMPCs in AL and MGUS exhibit high inter-individual heterogeneity, but not in healthy controls (Fig. 3a). Projection of the functional subset types showed that the predominant subset in AL and MGUS showed the highest deviation from the healthy controls, whereas subset 2, which produced the highest level of immunoglobulin, was more similar to them (Fig. 3b, S5a).

We then investigated the inter-individual differences in immunoglobulin production. The results confirmed that subset 2 had the highest expression of immunoglobulin genes in the AL, MGUS, and healthy controls. In addition, examination of differences in immunoglobulin idiotypes revealed that the expression of the light chain was highest in subset 2 in AL. However, kappa chains were relatively abundant in the non-monoclonal BMPCs of the healthy controls (Fig. 3d).

Since the immunoglobulin idiotype affects organ tropism of light chains and is associated with organ involvement in AL patients^{9, 21, 22}, we checked the monoclonality of several immunoglobulins including IGLV 1-44, IGLV 2-23, IGLV 3-19, IGLV 2-14, and IGLV 6-57, across samples (Fig. 3e). Previous studies demonstrated that IGLV 1-44 and IGLV 2-14 light chains tend to be deposited in cardiac muscles²³, whereas IGLV 3-19 damages renal glomeruli²⁴. Our findings on the clinical manifestations of AL patients (Table S1) are consistent with those of previous reports.

Additionally, we investigated the expression of drug-associated marker genes. We noted that BMPCs in a clinically significant proportion of AL and MGUS patients harboured t(11;14) (IgH/CCND1), a translocation conjugating the IgH promoter to cyclin D1 (CCND1). We observed predominant subsets in some cases of AL and MGUS that highly expressed CCND1, whereas cyclin D2 (CCND2) was upregulated in non-t(11;14) AL. In addition, CD27 was highly expressed in the healthy controls. CD38, the target of daratumumab, was expressed in BMPCs of AL, MGUS, and healthy subjects, and was subtly upregulated in non-t(11;14)AL (Fig. 3f).

We investigated the CNVs inferred from our single-cell transcriptome data and found patterns associated with t(11;14) and CCND1 expression (Fig. 3g, S5b). More specifically, the BMPCs of non-t(11;14)AL showed larger CNV fragments than those detected in patients with t(11;14) AL and MGUS. CNVs detected in BMPCs from t(11;14) AL samples were also found in t(11;14) MGUS, thus implicating a mutational continuum underlying the progression of MGUS to AL²⁵. In particular, a hotspot for CNVs specific to t(11;14) AL and MGUS was identified on the long arm of chromosome 11 around the CCND1 locus. Similarly, the deletion of 13q14 and gain of 1q21 are both mutations frequently found in AL²⁶, consistent with the CNVs observed on chromosome 13 and chromosome 1 in non-t(11;14) AL.

The combination of intra -and inter-individual heterogeneity revealed monoclonality, marker gene expression, and copy number variations of BMPCs in AL, especially those associated with t(11;14).

t(11;14) and non-t(11;14) AL had aberrant subsets that up-regulated amyloidosis-associated genes and those associated with light chain production

From the above findings on intra- and inter-individual heterogeneity in BMPCs, we observed that subset 5 was enriched in the AL (Fig. 1e, 4a). GSEA showed that the upregulated genes in subset 5 were enriched for amyloidosis-associated genes (DisGeNET database) in subset 5 of both t(11;14) (Fig. 4b) and non-t(11;14) AL (Fig. 4c). This subset upregulated genes encoding amyloid-beta binding proteins and secretory granule proteins, with APOE serving as the hub (Fig. 4d).

We also found a proportion of subset 2 that deviated from healthy controls (Fig. 4a). Compared with their counterparts in healthy controls (Fig. S6a), and MGUS (Fig. S6b), subset 2 in AL upregulated genes involved in the active transport of proteins to the Golgi apparatus, protein folding in the Golgi apparatus, neutrophil degranulation, asparagine N-linked protein glycosylation, and apoptotic pathways (Fig. 4e). Specifically, peptidylprolyl isomerase A (PPIA), B4GALT1, and transmembrane P24 trafficking protein 2 (TMED2) were shown to participate in multiple pathways (Fig. 4f).

Collectively, these findings showed that amyloidosis-associated genes and aberrant light-chain production-associated pathways were upregulated in the subsets related to AL pathogenesis in both t(11;14) and non-t(11;14) AL.

Predominant subset in t(11;14) and non-t(11;14) AL differentially expressed the gene associated with venetoclax sensitivity

In the t(11;14) and non-t(11;14) AL, the predominant subset showed subtle differences in gene expression (Fig 5a), and were highly associated with venetoclax sensitivity. Functional enrichment of differentially expressed genes revealed that t(11;14) AL upregulated B-cell differentiation pathways, whereas non-t(11;14) AL upregulated the response to growth factor stimuli. In addition, the predominant subset in t(11;14) AL up-regulated the glucose metabolic process and response to corticosteroids, while the non-t(11;14) AL group showed up-regulation of the blood vessel development, neutrophil degranulation, and Golgi-associated pathways (Fig. 5b).

Protein–protein interactions (PPI) network analysis showed that upregulated genes in the predominant subset in t(11;14) AL were in a network (Fig. 5c) and enriched in a gene set highly expressed in venetoclax-sensitive MM cell lines (Fig. 5d). The predominant subset in t(11;14) AL exhibited overexpression of anti-apoptotic BCL2 complex and B cell-associated genes, including CD79A, VPREB3, CD45, and MS4A1. In contrast, the upregulated genes in the predominant subset in non-t(11;14) AL included CCND2, PPIA, GAPDH, and S100A gene families and those encoding the cytochrome c protein family (Fig. 5e). They were significantly enriched in a gene set highly expressed in venetoclax-resistant MM cell lines (Fig. 5f).

Thus, the predominant subset contributed to the differential expression of venetoclax sensitivity-associated genes in t(11;14) and non-t(11;14) AL, which are involved in a functional PPI network.

Proportion of functional subsets defined the patient subgroups of AL exhibiting differential gene expression related to venetoclax sensitivity

Finally, we sought to validate these findings using bulk RNA-seq of BMPCs from an independent cohort of 29 patients with AL (Fig. S7a, S7b). Clustering the proportion of subsets revealed three subgroups of patients with AL (Fig. 6a, Fig. S7c). Subgroup B had a larger population of the predominant subset (subset 0), and subgroup A had a larger proportion of subset 1, involving the majority of patients with AL (Fig. 6b). Subgroups A and B showed mutually exclusive upregulation of CCND2 and CCND1, and another subgroup was double-negative for CCND2 and CCND1 (Fig. 6c, 6d).

We then validated the co-expression of genes associated with venetoclax sensitivity (Fig. 6e). CCND1, B-cell CLL/lymphoma 2 (BCL2), CD79A, and VPREB3 were co-expressed in subgroup B with a larger predominant subset, while CCND2, S100A6, and CST3 were co-expressed in subgroup A. These consistent proportions of subsets and marker gene expression demonstrated that the scRNA-seq samples of t(11;14) and non-t(11;14) AL represented subgroups B and A, respectively.

Taken together, these results validated a patient subgroup featuring a larger proportion of the predominant subset and a higher expression of CCND1 and venetoclax sensitivity-associated genes.

In the current study, we used scRNA-seq to define the functional subsets within BMPCs in AL, as well as their associations with amyloid light-chain production and venetoclax sensitivity. Seminal studies examining the bulk transcriptome of BMPCs implied major transcriptomic differences between MM and healthy controls, while the BMPCs in AL were similar to those in healthy controls^{27, 28}. Compared to bulk RNA-seq, scRNA-seq provides more insight into the role of pathogenic BMPCs in AL.

Based on our findings, we describe a mechanistic model involving functional subsets with different pathogenic roles in AL that follows the trajectory of a decreasing signalling entropy and concurrently increasing immunoglobulin production. Oncogenesis is likely initiated by DNA damage, thereby activating cell cycle-related DNA repair systems²⁹ in subset 6 with the highest signalling entropy. Downstream of subset 6 was the predominant subset (subset 0), which was a major target of glucocorticoids, bortezomib, and venetoclax (Fig. 2d). Subsets 1 and 5 were intermediate between the predominant subset and the highly differentiated subsets 2, 3, and 4 specified in immunoglobulin production.

In particular, the amyloidosis-associated genes aberrantly expressed in subset 5 included those encoding the amyloid-beta-binding proteins APOE and CST3 and involved in the pathogenesis of various types of amyloidosis (Fig. 4d). APOE is likely a “pathological molecular chaperone” that induces the beta-sheet conformation of amyloid proteins³⁰. CST3 binds amyloid-beta to inhibit the formation of fibrils³¹. We also observed APOE protein co-deposited with the aberrant light chain in the heart, kidney, and abdominal fat tissue of patients with AL using a mass spectrometry-based proteomic method³². Therefore, we hypothesise a potential mechanism for AL pathogenesis, i.e., the formation of amyloid beta-sheets activates a defence response to limit fibril formation by amyloid-beta-binding proteins.

In addition, the aberrant light chain-producing subset 2 enriched in AL likely upregulated N-linked protein glycosylation, transport to the Golgi apparatus, and subsequent modifications (Fig. 4e). Amyloidogenic light chains gain additional N-linked glycosylation sites through mutations³³. Previous studies have shown an increased N-linked glycosylation of pathogenic light chains secreted into the serum³⁴ and upregulation of protein N-linked glycosylation in BMPCs in AL¹². Given that glycosylation patterns are used by cells to monitor protein folding, dysregulation of this process implies misfolding of light chains in AL. PPIA is known to be a potential therapeutic target for relapsed MM because of its central role in the protein-folding response pathway,³⁵ which also participates in aberrant light chain-producing BMPCs in AL (Fig. 4f).

We comparatively studied the difference between t(11;14) and non-t(11;14) AL, which was associated with sensitivity to venetoclax in scRNA-seq and bulk RNA-seq analyses. A previous study reported the mutually exclusive overexpression of CCND1 and CCND2 in MM³⁶, which was consistent with the observation that t(11;14) AL highly expressed CCND1 and non-t(11;14) AL expressed CCND2. t(11;14) (IgH/CCND1) is known to be associated with a lower frequency of hyperdiploidy in AL³⁷, which was also confirmed by our results of the inferred CNVs (Fig. 3g).

In addition, we observed several cell surface markers in the single BMPCs (Fig. 3f). High expression of CD27 is reportedly associated with an improved prognosis for MM patients^{38, 39}, possibly because it is highly expressed in healthy BMPCs. CD38 was subtly upregulated in non-t(11;14) AL, indicating a potentially higher sensitivity of non-t(11;14) AL to daratumumab.

This study has several limitations. First, our cohort had a limited sample size, although we sequenced 14,081 BMPCs. Our analysis was limited to transcriptomic data; thus, we cannot directly infer the aberrant folding or accumulation of light chains from the scRNA-seq analysis. Although we focused on CCND1 expression, t(11;14) (IgH/CCND1) translocation, and CNVs inferred from the single-cell transcriptome, whole-genome sequencing would enable further characterisation of potential causative mutations related to the different AL types and aberrant BMPC functions.

Overall, our results revealed seven functionally distinct BMPC subsets and suggested possible mechanisms leading to amyloid light-chain production and venetoclax sensitivity. These results provide a basis for future investigations into AL pathogenesis, molecular subtypes, and amyloidosis-targeting therapies.

Acknowledgments

This study was supported by grants from the National Natural Science Foundation of China (81974011, 81890994), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-019, 2020-RC310-009), the Fundamental Research Funds for the Central Universities (3332021002), the National Basic Research Program of China (2018YFA0801402) and the National Undergraduate Training Programs for Innovation and Entrepreneurship (202110023012).

Authorship

Contribution: X.W. analyzed the data and drafted the work; H.H., J.S., Q.W., and X.G. collected the data and revised the work; K.S., L.Z., and Y.Z. collected the sample; X.C. and M.Q. revised the work; Y.C. and J. L. designed the project and revised the work. All the authors read and approved the final manuscript.

ORCID profile: X.W., 0000-0002-9424-6540; H.H., 0000-0002-7935-7170; X.G., 0000-0001-8032-060X; L.Z., 0000-0002-0860-9625; X.C., 0000-0001-7884-3073; Y.C., 0000-0001-9406-8273, J.L. 0000-0002-4549-0694

Data and materials availability

scRNA-seq data are deposited in the GEO database (GEO accession: GSE188222) and SRA (BioProject: PRJNA776426). For scRNA-seq data, code and Jupyter notebook are available in the GitHub repository (https://github.com/Selecton98/SingleCell_pALMGUS)

Merlini G, Dispenzieri A, Sanchorawala V, Schönland SO, Palladini G, Hawkins PN, et al. Systemic immunoglobulin light chain amyloidosis. Nature reviews Disease primers 2018 Oct 25; 4(1): 38.
Hasib Sidiqi M, Gertz MA. Immunoglobulin light chain amyloidosis diagnosis and treatment algorithm 2021. Blood cancer journal 2021 May 15; 11(5): 90.
Merlini G. Determining the significance of MGUS. Blood 2014 Jan 16; 123(3): 305–307.
Boyle EM, Ashby C, Wardell CP, Rowczenio D, Sachchithanantham S, Wang Y, et al. The genomic landscape of plasma cells in systemic light chain amyloidosis. Blood 2018 Dec 27; 132(26): 2775–2777.
Deshmukh M, Elderfield K, Rahemtulla A, Naresh KN. Immunophenotype of neoplastic plasma cells in AL amyloidosis. Journal of clinical pathology 2009 Aug; 62(8): 724–730.
Bochtler T, Hegenbart U, Kunz C, Granzow M, Benner A, Seckinger A, et al. Translocation t(11;14) is associated with adverse outcome in patients with newly diagnosed AL amyloidosis when treated with bortezomib-based regimens. Journal of clinical oncology: official journal of the American Society of Clinical Oncology 2015 Apr 20; 33(12): 1371–1378.
Nahi H, Kashif M, Klimkowska M, Karvouni M, Wallblom A, Gran C, et al. Low dose venetoclax as a single agent treatment of plasma cell malignancies harboring t(11;14). American journal of hematology 2021 Aug 1; 96(8): 925–933.
Sidiqi MH, Al Saleh AS, Leung N, Jevremovic D, Aljama MA, Gonsalves WI, et al. Venetoclax for the treatment of translocation (11;14) AL amyloidosis. Blood cancer journal 2020 May 11; 10(5): 55.
Comenzo RL, Zhang Y, Martinez C, Osman K, Herrera GA. The tropism of organ involvement in primary systemic amyloidosis: contributions of Ig V(L) germ line gene use and clonal plasma cell burden. Blood 2001 Aug 1; 98(3): 714–720.
Dutta AK, Alberge JB, Sklavenitis-Pistofidis R, Lightbody ED, Getz G, Ghobrial IM. Single-cell profiling of tumour evolution in multiple myeloma - opportunities for precision medicine. Nat Rev Clin Oncol 2022 Jan 11.
Ledergor G, Weiner A, Zada M, Wang SY, Cohen YC, Gatt ME, et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nature medicine 2018 Dec; 24(12): 1867–1876.
Alameda D, Goicoechea I, Vicari M, Arriazu E, Nevone A, Rodríguez S, et al. Tumor cells in light-chain amyloidosis and myeloma show different transcriptional rewiring of normal plasma cell development. Blood 2021 Jun 16.
Oetjen KA, Lindblad KE, Goswami M, Gui G, Dagur PK, Lai C, et al. Human bone marrow assessment by single-cell RNA sequencing, mass cytometry, and flow cytometry. JCI insight 2018 Dec 6; 3(23).
Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nature methods 2019 Dec; 16(12): 1289–1296.
McGinnis CS, Murrow LM, Gartner ZJ. DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell systems 2019 Apr 24; 8(4): 329–337.e324.
Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature immunology 2019 Feb; 20(2): 163–172.
Street K, Risso D, Fletcher RB, Das D, Ngai J, Yosef N, et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC genomics 2018 Jun 19; 19(1): 477.
Teschendorff AE, Enver T. Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome. Nature communications 2017 Jun 1; 8: 15599.
Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science (New York, NY) 2014 Jun 20; 344(6190): 1396–1401.
Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nature methods 2017 Nov; 14(11): 1083–1086.
Comenzo RL, Wally J, Kica G, Murray J, Ericsson T, Skinner M, et al. Clonal immunoglobulin light chain variable region germline gene use in AL amyloidosis: association with dominant amyloid-related organ involvement and survival after stem cell transplantation. British journal of haematology 1999 Sep; 106(3): 744–751.
Abraham RS, Geyer SM, Price-Troska TL, Allmer C, Kyle RA, Gertz MA, et al. Immunoglobulin light chain variable (V) region genes influence clinical presentation and outcome in light chain-associated amyloidosis (AL). Blood 2003 May 15; 101(10): 3801–3808.
Perfetti V, Palladini G, Casarini S, Navazza V, Rognoni P, Obici L, et al. The repertoire of λ light chains causing predominant amyloid heart involvement and identification of a preferentially involved germline gene, IGLV1-44. Blood 2012 Jan 5; 119(1): 144–150.
Pradhan T, Annamalai K, Sarkar R, Hegenbart U, Schönland S, Fändrich M, et al. Solid state NMR assignments of a human λ-III immunoglobulin light chain amyloid fibril. Biomolecular NMR assignments 2021 Apr; 15(1): 9–16.
Rossi A, Voigtlaender M, Janjetovic S, Thiele B, Alawi M, März M, et al. Mutational landscape reflects the biological continuum of plasma cell dyscrasias. Blood cancer journal 2017 Feb 24; 7(2): e537.
Bochtler T, Hegenbart U, Cremer FW, Heiss C, Benner A, Hose D, et al. Evaluation of the cytogenetic aberration pattern in amyloid light chain amyloidosis as compared with monoclonal gammopathy of undetermined significance reveals common pathways of karyotypic instability. Blood 2008 May 1; 111(9): 4700–4705.
Paiva B, Martinez-Lopez J, Corchete LA, Sanchez-Vega B, Rapado I, Puig N, et al. Phenotypic, transcriptomic, and genomic features of clonal plasma cells in light-chain amyloidosis. Blood 2016 Jun 16; 127(24): 3035–3039.
Abraham RS, Ballman KV, Dispenzieri A, Grill DE, Manske MK, Price-Troska TL, et al. Functional gene expression analysis of clonal plasma cells identifies a unique molecular profile for light chain amyloidosis. Blood 2005 Jan 15; 105(2): 794–803.
Mladenov E, Magin S, Soni A, Iliakis G. DNA double-strand-break repair in higher eukaryotes and its role in genomic instability and cancer: Cell cycle and proliferation-dependent regulation. Seminars in cancer biology 2016 Jun; 37–38: 51–64.
Wisniewski T, Frangione B. Apolipoprotein E: a pathological chaperone protein in patients with cerebral and systemic amyloid. Neurosci Lett 1992 Feb 3; 135(2): 235–238.
Kaeser SA, Herzig MC, Coomaraswamy J, Kilger E, Selenica ML, Winkler DT, et al. Cystatin C modulates cerebral beta-amyloidosis. Nature genetics 2007 Dec; 39(12): 1437–1439.
Sun W, Sun J, Zou L, Shen K, Zhong D, Zhou D, et al. The Successful Diagnosis and Typing of Systemic Amyloidosis Using A Microwave-Assisted Filter-Aided Fast Sample Preparation Method and LC/MS/MS Analysis. PloS one 2015; 10(5): e0127180.
Stevens FJ. Four structural risk factors identify most fibril-forming kappa light chains. Amyloid 2000 Sep; 7(3): 200–211.
Kumar S, Murray D, Dasari S, Milani P, Barnidge D, Madden B, et al. Assay to rapidly screen for immunoglobulin light chain glycosylation: a potential path to earlier AL diagnosis for a subset of patients. Leukemia 2019 Jan; 33(1): 254–257.
Cohen YC, Zada M, Wang SY, Bornstein C, David E, Moshe A, et al. Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing. Nature medicine 2021 Mar; 27(3): 491–503.
Alvarez-Benayas J, Trasanidis N, Katsarou A, Ponnusamy K, Chaidos A, May PC, et al. Chromatin-based, in cis and in trans regulatory rewiring underpins distinct oncogenic transcriptomes in multiple myeloma. Nature communications 2021 Sep 14; 12(1): 5450.
Bochtler T, Hegenbart U, Heiss C, Benner A, Moos M, Seckinger A, et al. Hyperdiploidy is less frequent in AL amyloidosis compared with monoclonal gammopathy of undetermined significance and inversely associated with translocation t(11;14). Blood 2011 Apr 7; 117(14): 3809–3815.
Chu B, Bao L, Wang Y, Lu M, Shi L, Gao S, et al. CD27 antigen negative expression indicates poor prognosis in newly diagnosed multiple myeloma. Clinical immunology (Orlando, Fla) 2020 Apr; 213: 108363.
Guikema JE, Hovenga S, Vellenga E, Conradie JJ, Abdulahad WH, Bekkema R, et al. CD27 is heterogeneously expressed in multiple myeloma: low CD27 expression in patients with high-risk disease. British journal of haematology 2003 Apr; 121(1): 36–43.

There is NO conflict of interest to disclose.

Download PDF

Version 1

posted

You are reading this latest preprint version

Functional Subsets of Plasma Cells Associated with Amyloid Production and Venetoclax Sensitivity in Light Chain Amyloidosis

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Results

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1