Computational dissection of immune reconstitution across diverse conditioning regimens in adults with sickle cell disease undergoing allogeneic stem cell transplantation

Abstract Background: Allogeneic hematopoietic stem-cell transplantation (HSCT) is curative for adult sickle-cell disease (SCD). Yet donor source (matched related [MRD] or unrelated [MUD], mismatched related [haplo] or unrelated [MMUD]), conditioning regimen (combination of in vivo T-cell depletion with single agent busulfan, fludarabine-melphalan, or TBI), and GVHD prophylaxis (post-transplant cyclophosphamide [PTCy], ex vivo T-cell depletion [e.g, CD34 selection], or sirolimus) each shape bilateral tolerance and immune reconstitution differently. Systematic, high-dimensional comparisons of immune recovery after HSCT in SCD remain scarce. Aims: Computationally map lymphoid and myeloid recovery in SCD over time across conditioning platforms, benchmarking patterns against healthy controls (HC). Methods: High-dimensional flow cytometry was performed on 70 peripheral-blood samples from 13 of 22 transplanted adults with SCD, alongside three HC specimens, using a 43-marker panel. After FlowCT quality control, data were biexponentially transformed, meta-clustered with FlowSOM, visualized with UMAP, and manually curated into lineages; subset frequencies and absolute counts were compared with Wilcoxon tests. Conditioning/GVHD-prophylaxis groups were 4 Gy + PTCy (MUD/MMUD, n = 3), FluMel + CD34-selected graft + sirolimus (MMUD/MUD/Haplo, n = 4), and 3 Gy TBI + sirolimus (MRD, n = 6). Timepoints were classified as baseline, early (< 30 d), intermediate (60–90 d), and late (> 180 d). Clinical variables, including engraftment kinetics and lineage chimerism, were analyzed in the full 22-patient cohort, while immunophenotypic trajectories were derived from the 13-patient flow subset. Results: At baseline, SCD exhibited a B-cell skew, with naïve B-cell frequency at 11.6% of CD45⁺ (IQR 7.1–15.1%) vs. 4.0% in HC (P = 0.02), and trends toward higher plasma cells and monocytes. Engraftment kinetics differed by platform: CD34-selected grafts yielded the fastest ANC recovery (median 19 d, range 13–21) vs. TBI (median 24 d, 12–30) and PTCy (26 d, 22–31) (P = 0.005), whereas platelet recovery was quickest with TBI (7 d, 7–26) compared with CD34 (20.5 d, 16–38) and PTCy (29 d, 22–35) (P = 0.03). In line with the rapid engraftment and neutrophil recovery, CD34 selection drove a myeloid-dominant landscape—monocytes at 62.7% (53.6–67.4%) vs. TBI at 50.0% and PTCy at 45.7% (P = 0.049) during the early time period (<30 d), accompanied by an innate lymphoid cell surge (7.7% vs ≤ 2.6%; P = 0.03). PTCy uniquely expanded plasma cells (26.8 × 10³ cells L⁻¹; 17.4–36.1) during this period. At 60–90 d, CD34 recipients showed rapid B-cell reconstitution: naïve B-cells 30.8% (18.7–39.7; 419 × 10³ cells L⁻¹) vs. 4.7% (48 cells L⁻¹) with TBI and 0.9% (6 cells L⁻¹) with PTCy (P ≤ 0.017). In contrast, PTCy favored plasma-cell (1.6%) and NK-cell (16.7%) dominance (P < 0.025). Day +100 chimerism was uniformly high—myeloid 99%, total PB 96%, B-cells 95%—with variable T-cell chimerism (CD34 50%, TBI 33%, PTCy 19%; P= 0.24). Beyond 180 d, CD34 grafts maintained higher T-cell numbers (CD4 116 vs 75 cells L⁻¹, P = 0.049; CD8 134 vs 21, P = 0.026) and preserved unconventional subsets (double-negative T 8.2 vs 0.6 cells L⁻¹, P = 0.01; innate-like T 40.6 vs 8.6, P = 0.049), whereas 3 Gy TBI favored memory B-cells (1.5% vs 0.3%, P = 0.049); late PTCy samples were unavailable. At one year, T-cell chimerism differed significantly (CD34 77%, TBI 58%, PTCy 35%; P = 0.047). Myeloid (99%) and B-cell (94%) chimerism remained high with no inter-group differences, while total PB chimerism trended lower with PTCy (55%) than with TBI (91%) or CD34 (98%) (P = 0.26). Conclusions: CD34-selected grafts accelerate early myeloid and naïve-B-cell rebound and foster robust CD4/CD8 T-cell reconstitution later. PTCy favors early NK- and plasma-cell expansion, whereas low-dose TBI promotes dendritic and memory-B recovery. These divergent trajectories mirror lineage-specific mixed chimerism, as full donor engraftment is uncommon across all compartments. Sub-lineage chimerism assays are therefore critical to dissect platform-specific immune recovery, bilateral tolerance, and graft-rejection risk.

Authors

Institutions

Publication Details

Journal
Blood
Published
2025-11-03
DOI
https://doi.org/10.1182/blood-2025-4255
Primary Topic
Hemoglobinopathies and Related Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Computational dissection of immune reconstitution across diverse conditioning regimens in adults with sickle cell disease undergoing allogeneic stem cell transplantation

Markus Y. Mapara, Christian Gordillo, Rajshekhar Chakraborty, Suzanne Lentzsch et al.
Blood
Hemoglobinopathies and Related Disorders
article

Computational dissection of immune reconstitution across diverse conditioning regimens in adults with sickle cell disease undergoing allogeneic stem cell transplantation

Markus Y. Mapara, Christian Gordillo, Rajshekhar Chakraborty, Suzanne Lentzsch, Monica Bhatia, Samuel Pan, Diane M. George, Simon Zhen, Abdul Hamid Bazarbachi, Amer Assal, Andrew Eisenberger, David Diuguid, Huihui Ma, Ran Reshef, Divaya Bhutani, Miriam Klahr
article en

Abstract

Abstract Background: Allogeneic hematopoietic stem-cell transplantation (HSCT) is curative for adult sickle-cell disease (SCD). Yet donor source (matched related [MRD] or unrelated [MUD], mismatched related [haplo] or unrelated [MMUD]), conditioning regimen (combination of in vivo T-cell depletion with single agent busulfan, fludarabine-melphalan, or TBI), and GVHD prophylaxis (post-transplant cyclophosphamide [PTCy], ex vivo T-cell depletion [e.g, CD34 selection], or sirolimus) each shape bilateral tolerance and immune reconstitution differently. Systematic, high-dimensional comparisons of immune recovery after HSCT in SCD remain scarce. Aims: Computationally map lymphoid and myeloid recovery in SCD over time across conditioning platforms, benchmarking patterns against healthy controls (HC). Methods: High-dimensional flow cytometry was performed on 70 peripheral-blood samples from 13 of 22 transplanted adults with SCD, alongside three HC specimens, using a 43-marker panel. After FlowCT quality control, data were biexponentially transformed, meta-clustered with FlowSOM, visualized with UMAP, and manually curated into lineages; subset frequencies and absolute counts were compared with Wilcoxon tests. Conditioning/GVHD-prophylaxis groups were 4 Gy + PTCy (MUD/MMUD, n = 3), FluMel + CD34-selected graft + sirolimus (MMUD/MUD/Haplo, n = 4), and 3 Gy TBI + sirolimus (MRD, n = 6). Timepoints were classified as baseline, early (< 30 d), intermediate (60–90 d), and late (> 180 d). Clinical variables, including engraftment kinetics and lineage chimerism, were analyzed in the full 22-patient cohort, while immunophenotypic trajectories were derived from the 13-patient flow subset. Results: At baseline, SCD exhibited a B-cell skew, with naïve B-cell frequency at 11.6% of CD45⁺ (IQR 7.1–15.1%) vs. 4.0% in HC (P = 0.02), and trends toward higher plasma cells and monocytes. Engraftment kinetics differed by platform: CD34-selected grafts yielded the fastest ANC recovery (median 19 d, range 13–21) vs. TBI (median 24 d, 12–30) and PTCy (26 d, 22–31) (P = 0.005), whereas platelet recovery was quickest with TBI (7 d, 7–26) compared with CD34 (20.5 d, 16–38) and PTCy (29 d, 22–35) (P = 0.03). In line with the rapid engraftment and neutrophil recovery, CD34 selection drove a myeloid-dominant landscape—monocytes at 62.7% (53.6–67.4%) vs. TBI at 50.0% and PTCy at 45.7% (P = 0.049) during the early time period (<30 d), accompanied by an innate lymphoid cell surge (7.7% vs ≤ 2.6%; P = 0.03). PTCy uniquely expanded plasma cells (26.8 × 10³ cells L⁻¹; 17.4–36.1) during this period. At 60–90 d, CD34 recipients showed rapid B-cell reconstitution: naïve B-cells 30.8% (18.7–39.7; 419 × 10³ cells L⁻¹) vs. 4.7% (48 cells L⁻¹) with TBI and 0.9% (6 cells L⁻¹) with PTCy (P ≤ 0.017). In contrast, PTCy favored plasma-cell (1.6%) and NK-cell (16.7%) dominance (P < 0.025). Day +100 chimerism was uniformly high—myeloid 99%, total PB 96%, B-cells 95%—with variable T-cell chimerism (CD34 50%, TBI 33%, PTCy 19%; P= 0.24). Beyond 180 d, CD34 grafts maintained higher T-cell numbers (CD4 116 vs 75 cells L⁻¹, P = 0.049; CD8 134 vs 21, P = 0.026) and preserved unconventional subsets (double-negative T 8.2 vs 0.6 cells L⁻¹, P = 0.01; innate-like T 40.6 vs 8.6, P = 0.049), whereas 3 Gy TBI favored memory B-cells (1.5% vs 0.3%, P = 0.049); late PTCy samples were unavailable. At one year, T-cell chimerism differed significantly (CD34 77%, TBI 58%, PTCy 35%; P = 0.047). Myeloid (99%) and B-cell (94%) chimerism remained high with no inter-group differences, while total PB chimerism trended lower with PTCy (55%) than with TBI (91%) or CD34 (98%) (P = 0.26). Conclusions: CD34-selected grafts accelerate early myeloid and naïve-B-cell rebound and foster robust CD4/CD8 T-cell reconstitution later. PTCy favors early NK- and plasma-cell expansion, whereas low-dose TBI promotes dendritic and memory-B recovery. These divergent trajectories mirror lineage-specific mixed chimerism, as full donor engraftment is uncommon across all compartments. Sub-lineage chimerism assays are therefore critical to dissect platform-specific immune recovery, bilateral tolerance, and graft-rejection risk.

BloodVol. 146(Supplement 1)
Columbia University Irving Medical Center (US)
Openalex Percentile: Top 42%
Hemoglobinopathies and Related Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.