Back

Spatial multi-omics of multiple myeloma uncovers niche-dependent pro-myeloma and immunosuppressive signaling in the bone marrow and extramedullary lesions

Ohlstrom, D. J.; Michaud, M.; Bakhtiari, M.; Vieira Dos Santos, J.; Pilcher, W. C.; Staub, A.; Satpathy, S.; Ferguson, K.; Mantrala, S.; Kim-Schulze, S.; Chen, Z.; Lonial, S.; Kemp, M. L.; Sherbenou, D.; Lagana, A.; Jaye, D. L.; Nooka, A.; Parekh, S.; Bhasin, M.

2026-03-25 cancer biology
10.64898/2026.03.23.713195 bioRxiv
Show abstract

Multiple myeloma (MM) is a plasma cell malignancy shaped by dynamic interactions between MM cells and non-malignant cells in the immune microenvironment. To spatially profile the influence of cellular context on MM and immune cell expression, we developed a multimodal framework integrating 10x Genomics Visium HD, 10x Genomics Xenium, and clinically annotated single-cell RNA (scRNA-seq) sequencing datasets. Visium HD enabled unbiased, whole transcriptome, spatial discovery at 16 {micro}m resolution, Xenium provided orthogonal validation at single-cell resolution, and scRNA-seq extended findings by mapping spatial labels and leveraging the greater sequencing depth. We developed a custom framework for cell type annotation within Visium HD spatial bins. Our approach enabled identification of plasma cell-dense niches enriched for non-canonical Wnt signaling, associated with gene expression supporting cell adhesion mediated drug resistance, inferior progression-free survival, and extramedullary lesions. Immune cells within these neighborhoods exhibited suppressed transcriptional states, including increased inhibitory receptor expression such as LAG3. Utilizing the niche-driven transcriptional states in MM and immune cells, we were able to develop a 15-gene signature independently predictive of progression free survival (HR = 2.00, p < 0.0001). Collectively, this study demonstrates the potential of integrated spatial and single-cell transcriptomics to define niche-specific programs supporting MM progression.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Nature Communications
4913 papers in training set
Top 11%
14.1%
2
Cancer Cell
38 papers in training set
Top 0.1%
9.0%
3
Genome Medicine
154 papers in training set
Top 1%
6.3%
4
Cancer Discovery
61 papers in training set
Top 0.4%
4.8%
5
Nature Cancer
35 papers in training set
Top 0.1%
4.8%
6
Advanced Science
249 papers in training set
Top 6%
3.5%
7
Cell Reports Medicine
140 papers in training set
Top 2%
3.5%
8
Cell Genomics
162 papers in training set
Top 1%
3.5%
9
Cancer Research
116 papers in training set
Top 1%
3.2%
50% of probability mass above
10
Cell Reports
1338 papers in training set
Top 18%
2.7%
11
Cell
370 papers in training set
Top 9%
2.3%
12
Science Translational Medicine
111 papers in training set
Top 2%
1.9%
13
Cell Reports Methods
141 papers in training set
Top 2%
1.9%
14
Blood
67 papers in training set
Top 0.7%
1.9%
15
Clinical Cancer Research
58 papers in training set
Top 0.9%
1.8%
16
eLife
5422 papers in training set
Top 40%
1.8%
17
Journal of Clinical Investigation
164 papers in training set
Top 3%
1.8%
18
Nature Genetics
240 papers in training set
Top 5%
1.6%
19
Leukemia
39 papers in training set
Top 0.5%
1.6%
20
Science Advances
1098 papers in training set
Top 20%
1.5%
21
Genome Biology
555 papers in training set
Top 5%
1.3%
22
JCI Insight
241 papers in training set
Top 5%
1.3%
23
Communications Biology
886 papers in training set
Top 17%
0.9%
24
Blood Advances
54 papers in training set
Top 1%
0.9%
25
Cell Systems
167 papers in training set
Top 11%
0.9%
26
Nature Immunology
71 papers in training set
Top 2%
0.8%
27
Proceedings of the National Academy of Sciences
2130 papers in training set
Top 43%
0.8%
28
Nature Cell Biology
99 papers in training set
Top 5%
0.7%
29
Journal of Experimental Medicine
106 papers in training set
Top 4%
0.7%
30
Nucleic Acids Research
1128 papers in training set
Top 18%
0.7%