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To do this, we first performed comparative transcriptomic analysis of LECs from healthy kidneys, rejection, and CKD ( Supplemental Data 5–7 ).
|
[
{
"end": 74,
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"start": 70,
"text": "LECs"
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{
"end": 95,
"label": "Tissue",
"start": 80,
"text": "healthy kidneys"
}
] |
Single_Cell
|
Source paper: PMC12435838
GO revealed that LECs from rejecting allografts were enriched for pathways related to the negative regulation of viral process (GO:0048525, fold-enrichment = 90.26, FDR = 5.95 × 10 ), including IFN-induced transmembrane proteins IFITM2 (log 2 FC = 1.76, P = 5.89 × 10 ) and IFITM3 (log 2 FC = 1.62, P = 6.86 × 10 ) ( Figure 7A ).
|
[
{
"end": 49,
"label": "CellType",
"start": 45,
"text": "LECs"
},
{
"end": 75,
"label": "Tissue",
"start": 55,
"text": "rejecting allografts"
}
] |
Single_Cell
|
IFN-γ was specifically enriched in T cells and NK cells in our scRNA-Seq dataset ( Figure 7B ), whereas other IFN types were not detected.
|
[
{
"end": 42,
"label": "CellType",
"start": 35,
"text": "T cells"
},
{
"end": 55,
"label": "CellType",
"start": 47,
"text": "NK cells"
}
] |
Single_Cell
|
We then examined an IFN-γ response signature — including levels of IFITM2, IFITM3, and the IFN-γ receptor subunits IFNGR1 and IFNGR2 — which was prominent in LECs and in blood endothelial cells and macrophages from rejecting allografts ( Figure 7C ).
|
[
{
"end": 209,
"label": "CellType",
"start": 198,
"text": "macrophages"
},
{
"end": 162,
"label": "CellType",
"start": 158,
"text": "LECs"
},
{
"end": 193,
"label": "CellType",
"start": 170,
"text": "blood endothelial cells"
},
{
"end": 235,
"label": "Tissue",
"start": 215,
"text": "rejecting allografts"
}
] |
Single_Cell
|
To contextualize this response, we compared the LEC profile in chronic rejection with that of HEVs, identified by enrichment for PNAd ( NTAN1 ) and downregulation of Notch pathway genes RBPJ and JAG1 ( Supplemental Figure 5B ) ( 83 , 84 ).
|
[
{
"end": 98,
"label": "Tissue",
"start": 94,
"text": "HEVs"
}
] |
Single_Cell
|
Unlike LECs, HEVs lacked lymphatic markers PROX1 and PDPN ( Supplemental Figure 5C ).
|
[
{
"end": 11,
"label": "CellType",
"start": 7,
"text": "LECs"
},
{
"end": 17,
"label": "Tissue",
"start": 13,
"text": "HEVs"
}
] |
Single_Cell
|
Instead, they expressed transcripts involved in leukocyte recruitment, activation, and regulation, such as CXCL16 , fractalkine ( CX3CL1 ), CD40, and IL-32 ( Supplemental Figure 5D and Supplemental Data 8 ), highlighting a distinct immune regulatory profile compared with LECs.
|
[
{
"end": 276,
"label": "CellType",
"start": 272,
"text": "LECs"
}
] |
Single_Cell
|
Source paper: PMC12435838
We next explored potential ligand-receptor interactions between LECs and lymphocytes using CellPhoneDB ( 85 ).
|
[
{
"end": 112,
"label": "CellType",
"start": 101,
"text": "lymphocytes"
},
{
"end": 96,
"label": "CellType",
"start": 92,
"text": "LECs"
}
] |
Single_Cell
|
Predicted cell-cell communication was highest in rejecting kidneys compared with CKD or healthy controls ( Supplemental Figure 6A ), with most interactions occurring between LECs and T cell subsets ( Supplemental Figure 6B ).
|
[
{
"end": 66,
"label": "Tissue",
"start": 49,
"text": "rejecting kidneys"
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{
"end": 178,
"label": "CellType",
"start": 174,
"text": "LECs"
},
{
"end": 197,
"label": "CellType",
"start": 183,
"text": "T cell subsets"
}
] |
Single_Cell
|
These included IFN-γ–IFNGR signaling from CD8 T cells to LECs across both control and rejecting kidneys ( Supplemental Figure 6C ).
|
[
{
"end": 53,
"label": "CellType",
"start": 42,
"text": "CD8 T cells"
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{
"end": 61,
"label": "CellType",
"start": 57,
"text": "LECs"
},
{
"end": 103,
"label": "Tissue",
"start": 86,
"text": "rejecting kidneys"
}
] |
Single_Cell
|
Chemokine-based interactions included established axes such as CCL21 , CCL2 , and ACKR2 ( Supplemental Figure 7A ), although CCL14/ACKR2 signaling with CD4 effector T cells was reduced in rejection.
|
[
{
"end": 172,
"label": "CellType",
"start": 152,
"text": "CD4 effector T cells"
}
] |
Single_Cell
|
Many chemokine receptors for ACKR2 ligands, including CCR2, CCR5, and CCR7, were expressed by T cells ( Supplemental Figure 7B ).
|
[
{
"end": 101,
"label": "CellType",
"start": 94,
"text": "T cells"
}
] |
Single_Cell
|
Source paper: PMC12435838
Notably, most of the remaining predicted interactions were coinhibitory in nature.
|
[] |
Single_Cell
|
These included LEC expression of poliovirus receptor ( PVR ) and galectin 9 ( LGALS9 ), which suppress effector T cell responses via TIGIT and HAVCR2 signaling, respectively ( 86 ) ( Figure 7D ).
|
[] |
Single_Cell
|
While also present in CKD and non-alloimmune graft injury ( Supplemental Figure 8 , A–C), these interactions had higher signaling scores in chronic rejection ( Figure 7E ).
|
[] |
Single_Cell
|
Immunostaining confirmed PVR expression on PDPN lymphatics in direct contact with CD4 T cells in rejecting allografts ( Figure 7F ).
|
[
{
"end": 58,
"label": "Tissue",
"start": 43,
"text": "PDPN lymphatics"
},
{
"end": 93,
"label": "CellType",
"start": 82,
"text": "CD4 T cells"
},
{
"end": 117,
"label": "Tissue",
"start": 97,
"text": "rejecting allografts"
}
] |
Single_Cell
|
When stimulated by IFN-γ, blood endothelia express PVR and LGALS9 to dampen T cell responses ( 87 , 88 ).
|
[
{
"end": 42,
"label": "Tissue",
"start": 26,
"text": "blood endothelia"
}
] |
Single_Cell
|
To examine whether this was the case for LECs, we stimulated a human LEC line with recombinant IFN-γ.
|
[
{
"end": 45,
"label": "CellType",
"start": 41,
"text": "LECs"
},
{
"end": 77,
"label": "CellLine",
"start": 63,
"text": "human LEC line"
}
] |
Single_Cell
|
LGALS9 transcripts were significantly upregulated after 24 hours (mean FC = 9.05, 95% CI = 5.37–12.73, adjusted P = 0.0002) and remained elevated at 48 hours (mean FC = 5.10, 95% CI = 1.42–8.78, adjusted P = 0.0093) ( Figure 7G ).
|
[] |
Single_Cell
|
Corresponding increases in LEC-secreted LGALS9 protein were observed at 48 hours (difference in mean concentratio n = 5.54 ng/mL, 95% CI = 3.26–7.83, adjusted P = 0.0002) and 72 hours (difference in mean concentratio n = 16.87 ng/mL, 95% CI = 14.58–19.16, adjusted P < 0.0001) ( Figure 7H ), confirming that LECs can acquire a coinhibitory profile in response to IFN-γ exposure.
|
[
{
"end": 312,
"label": "CellType",
"start": 308,
"text": "LECs"
}
] |
Single_Cell
|
Source paper: PMC12435838
However, in solid organ transplantation, IFN-γ–induced expression of HLAs on endothelial cells can facilitate alloantigen presentation and antibody binding to donor vasculature ( 89 , 90 ).
|
[
{
"end": 122,
"label": "CellType",
"start": 105,
"text": "endothelial cells"
},
{
"end": 204,
"label": "Tissue",
"start": 187,
"text": "donor vasculature"
}
] |
Single_Cell
|
Similarly, we found rejected allograft LECs also expressed HLA-DP and HLA-DR ( Figure 8A ).
|
[
{
"end": 43,
"label": "CellType",
"start": 20,
"text": "rejected allograft LECs"
}
] |
Single_Cell
|
To determine whether lymphatics were of donor or recipient origin, we assessed genotype using single-nucleotide variant calling, and found a majority of LECs were donor derived, with a small recipient cell contribution ( n = 3/247, 1.2%) ( Figure 8B ), consistent with a previous study of sex-mismatched renal allografts ( 91 ).
|
[
{
"end": 31,
"label": "Tissue",
"start": 21,
"text": "lymphatics"
},
{
"end": 157,
"label": "CellType",
"start": 153,
"text": "LECs"
},
{
"end": 320,
"label": "Tissue",
"start": 289,
"text": "sex-mismatched renal allografts"
}
] |
Single_Cell
|
Immunostaining for HLA-DR in chronic rejection ( Figure 8C ) demonstrated its expression on CD31 blood endothelial cells ( Figure 8D ), CD68 macrophages ( Figure 8E ) ( 92 , 93 ), and PDPN lymphatics ( Figure 8, F and G ).
|
[
{
"end": 120,
"label": "CellType",
"start": 92,
"text": "CD31 blood endothelial cells"
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{
"end": 152,
"label": "CellType",
"start": 136,
"text": "CD68 macrophages"
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{
"end": 199,
"label": "Tissue",
"start": 184,
"text": "PDPN lymphatics"
}
] |
Single_Cell
|
Importantly, we detected complement factor C4d deposition, a histological hallmark of alloantibody-mediated complement activation, on PDPN lymphatic vessels in 2 rejecting allografts from patients with de novo donor-specific antibodies ( Figure 8H ).
|
[
{
"end": 156,
"label": "Tissue",
"start": 134,
"text": "PDPN lymphatic vessels"
},
{
"end": 182,
"label": "Tissue",
"start": 162,
"text": "rejecting allografts"
}
] |
Single_Cell
|
These HLA-DR lymphatic regions were surrounded by CD3 T cells ( Supplemental Video 10 ), suggesting coordinated alloantibody and T cell engagement.
|
[
{
"end": 30,
"label": "Tissue",
"start": 6,
"text": "HLA-DR lymphatic regions"
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{
"end": 61,
"label": "CellType",
"start": 50,
"text": "CD3 T cells"
}
] |
Single_Cell
|
Together, these data demonstrate that LECs in chronic rejection acquire an IFN-γ–responsive, immune-inhibitory transcriptional phenotype, marked by coinhibitory ligand expression, HLA class II upregulation, and evidence of complement activation.
|
[
{
"end": 42,
"label": "CellType",
"start": 38,
"text": "LECs"
}
] |
Single_Cell
|
Source paper: PMC12435838
Lymphatic vessels play a central role in maintaining fluid balance and immune homeostasis, yet their structural and molecular features in the human kidney remain underexplored.
|
[
{
"end": 45,
"label": "Tissue",
"start": 28,
"text": "Lymphatic vessels"
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{
"end": 182,
"label": "Tissue",
"start": 170,
"text": "human kidney"
}
] |
Single_Cell
|
This gap is clinically relevant, as lymphangiogenesis occurs across a range of kidney diseases ( 11 – 14 ), and augmenting lymphatic function confers therapeutic benefit in preclinical models of kidney disease ( 94 – 96 ), hypertension ( 97 – 99 ), and acute kidney transplant rejection ( 29 ).
|
[] |
Single_Cell
|
Here, we combined 3D imaging of optically cleared tissue with scRNA-Seq to resolve the spatial architecture and molecular identity of lymphatics in the healthy human kidney and to interrogate their remodeling in chronic transplant rejection.
|
[
{
"end": 144,
"label": "Tissue",
"start": 134,
"text": "lymphatics"
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{
"end": 172,
"label": "Tissue",
"start": 152,
"text": "healthy human kidney"
}
] |
Single_Cell
|
Although previous studies have identified lymphatics in the kidney hilum and cortex ( 11 – 14 ), our 3D imaging approach yielded potentially new spatial insights, including a hierarchical arrangement of kidney lymphatics and the initiation of blind ends near proximal and distal tubular nephron segments, key sites of reabsorption and solute exchange between the urinary filtrate and blood.
|
[
{
"end": 389,
"label": "Tissue",
"start": 384,
"text": "blood"
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{
"end": 83,
"label": "Tissue",
"start": 77,
"text": "cortex"
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{
"end": 52,
"label": "Tissue",
"start": 42,
"text": "lymphatics"
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{
"end": 72,
"label": "Tissue",
"start": 60,
"text": "kidney hilum"
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{
"end": 220,
"label": "Tissue",
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"text": "kidney lymphatics"
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{
"end": 303,
"label": "Tissue",
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"text": "blind ends near proximal and distal tubular nephron segments"
},
{
"end": 379,
"label": "Tissue",
"start": 363,
"text": "urinary filtrate"
}
] |
Single_Cell
|
Using scRNA-Seq, we defined a transcriptional census of human kidney LECs, identifying expression of molecules previously characterized in other lymphatic beds but not in human kidney LECs, such as FABP4 ( 100 , 101 ) and ANGPT2 ( 102 – 104 ).
|
[
{
"end": 73,
"label": "CellType",
"start": 56,
"text": "human kidney LECs"
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{
"end": 159,
"label": "Tissue",
"start": 145,
"text": "lymphatic beds"
},
{
"end": 188,
"label": "CellType",
"start": 171,
"text": "human kidney LECs"
}
] |
Single_Cell
|
Source paper: PMC12435838
A recent analysis has transcriptionally profiled a population of LECs in the lymph node ( 105 ).
|
[
{
"end": 115,
"label": "Tissue",
"start": 105,
"text": "lymph node"
},
{
"end": 97,
"label": "CellType",
"start": 93,
"text": "LECs"
}
] |
Single_Cell
|
Our findings further extend the evidence for organ-specific heterogeneity of human lymphatics.
|
[
{
"end": 93,
"label": "Tissue",
"start": 77,
"text": "human lymphatics"
}
] |
Single_Cell
|
Compared with lymphatics from barrier tissues such as skin, lung, and intestines, kidney LECs displayed reduced expression of genes encoding classical immune trafficking molecules like CXCL8 and LYVE1, the latter confirmed at the protein level and also recently corroborated in mouse kidneys ( 106 ).
|
[
{
"end": 80,
"label": "Tissue",
"start": 70,
"text": "intestines"
},
{
"end": 64,
"label": "Tissue",
"start": 60,
"text": "lung"
},
{
"end": 58,
"label": "Tissue",
"start": 54,
"text": "skin"
},
{
"end": 45,
"label": "Tissue",
"start": 14,
"text": "lymphatics from barrier tissues"
},
{
"end": 93,
"label": "CellType",
"start": 82,
"text": "kidney LECs"
},
{
"end": 291,
"label": "Tissue",
"start": 278,
"text": "mouse kidneys"
}
] |
Single_Cell
|
Instead, kidney LECs express a repertoire of other molecules, including DNASE1L3 , a molecule involved in extracellular DNA clearance and deficiency of which is implicated in lupus nephritis ( 107 – 109 ).
|
[
{
"end": 20,
"label": "CellType",
"start": 9,
"text": "kidney LECs"
}
] |
Single_Cell
|
Such findings could suggest tissue-specific adaptations of the lymphatic regulation of immunity and may inform future studies of immune-mediated kidney disease.
|
[] |
Single_Cell
|
Although lymphatic valve markers were sparsely detected, unlike in mouse kidneys ( 110 ), we identified transcriptional heterogeneity among kidney LECs, including a subpopulation enriched for CCL2 and CXCL2 .
|
[
{
"end": 80,
"label": "Tissue",
"start": 67,
"text": "mouse kidneys"
},
{
"end": 151,
"label": "CellType",
"start": 140,
"text": "kidney LECs"
},
{
"end": 206,
"label": "CellType",
"start": 165,
"text": "subpopulation enriched for CCL2 and CXCL2"
}
] |
Single_Cell
|
This is reminiscent of molecularly distinct and immune-interacting LEC subsets in the nasal mucosa ( 111 , 112 ) and dermis ( 113 ).
|
[
{
"end": 123,
"label": "Tissue",
"start": 117,
"text": "dermis"
},
{
"end": 78,
"label": "CellType",
"start": 67,
"text": "LEC subsets"
},
{
"end": 98,
"label": "Tissue",
"start": 86,
"text": "nasal mucosa"
}
] |
Single_Cell
|
This heterogeneity may arise, in part, from microenvironment signals, such as IFN-γ, which drive context-dependent reprogramming of LECs in inflammation or cancer ( 114 – 116 ).
|
[
{
"end": 136,
"label": "CellType",
"start": 132,
"text": "LECs"
}
] |
Single_Cell
|
We show that LECs upregulate PVR and LGALS9 in response to IFN-γ, echoing responses in the blood endothelium ( 87 , 88 ) and supporting a paradigm in which the behavior of lymphatics is actively shaped by their surrounding milieu.
|
[
{
"end": 17,
"label": "CellType",
"start": 13,
"text": "LECs"
},
{
"end": 108,
"label": "Tissue",
"start": 91,
"text": "blood endothelium"
},
{
"end": 182,
"label": "Tissue",
"start": 172,
"text": "lymphatics"
}
] |
Single_Cell
|
Source paper: PMC12435838
In kidney transplantation, lymphatics have been associated with improved graft survival, possibly through increased leukocyte clearance ( 27 – 29 ), but also with immune activation and fibrosis ( 23 , 25 , 31 , 117 , 118 ).
|
[
{
"end": 65,
"label": "Tissue",
"start": 55,
"text": "lymphatics"
}
] |
Single_Cell
|
Our findings challenge the notion that lymphangiogenesis is uniformly pathogenic.
|
[] |
Single_Cell
|
Although we observed lymphangiogenesis and proximity of these vessels to TLSs in rejecting allografts, we showed that allograft LECs acquire a tolerogenic transcriptional program driven by IFN-γ.
|
[
{
"end": 69,
"label": "Tissue",
"start": 62,
"text": "vessels"
},
{
"end": 101,
"label": "Tissue",
"start": 81,
"text": "rejecting allografts"
},
{
"end": 132,
"label": "CellType",
"start": 118,
"text": "allograft LECs"
},
{
"end": 77,
"label": "Tissue",
"start": 73,
"text": "TLSs"
}
] |
Single_Cell
|
LEC-derived immune-inhibitory ligands dampen effector T cell function in cancer ( 119 , 120 ), neuroinflammation ( 121 ), and infection ( 69 ), and we confirmed the expression of 2 exemplar molecular candidates, PVR and LGALS9, at both the transcript and protein level.
|
[] |
Single_Cell
|
Source paper: PMC12435838
However, this tolerogenic molecular program coincides with structural perturbations to allograft lymphatics.
|
[
{
"end": 135,
"label": "Tissue",
"start": 115,
"text": "allograft lymphatics"
}
] |
Single_Cell
|
In rejection, lymphatics exhibited loss of hierarchical organization, infiltration into the medulla, and transformation of cell-cell junctions from button- to zipper-like morphology, changes known to impair fluid and cell transport ( 68 – 70 ).
|
[
{
"end": 24,
"label": "Tissue",
"start": 14,
"text": "lymphatics"
},
{
"end": 99,
"label": "Tissue",
"start": 92,
"text": "medulla"
}
] |
Single_Cell
|
Building on previous studies in kidney ( 26 , 27 ) and other inflammatory contexts ( 80 , 122 , 123 ), we identified TLSs of varying maturity positioned along lymphatic networks.
|
[
{
"end": 38,
"label": "Tissue",
"start": 32,
"text": "kidney"
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{
"end": 121,
"label": "Tissue",
"start": 117,
"text": "TLSs"
},
{
"end": 177,
"label": "Tissue",
"start": 159,
"text": "lymphatic networks"
}
] |
Single_Cell
|
Given the potential for in situ antigen presentation and T cell activation within the TLS ( 75 , 77 , 78 , 124 – 127 ), and given the observed altered localization of CD4 T cells within and around lymphatic vessels, it is tempting to speculate that lymphatic perturbation may contribute to CD4 T cell retention within allografts, heralding the formation and maintenance of the TLS in chronic rejection.
|
[
{
"end": 89,
"label": "Tissue",
"start": 86,
"text": "TLS"
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{
"end": 178,
"label": "CellType",
"start": 167,
"text": "CD4 T cells"
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{
"end": 214,
"label": "Tissue",
"start": 197,
"text": "lymphatic vessels"
},
{
"end": 328,
"label": "Tissue",
"start": 318,
"text": "allografts"
},
{
"end": 380,
"label": "Tissue",
"start": 377,
"text": "TLS"
}
] |
Single_Cell
|
Additionally, we demonstrate that allograft LECs express HLA class II and show C4d deposition in patients with de novo donor-specific antibodies, consistent with alloantibody targeting and complement activation.
|
[
{
"end": 48,
"label": "CellType",
"start": 34,
"text": "allograft LECs"
}
] |
Single_Cell
|
Analogous injury to the blood vasculature ( 19 ) is well-characterized in transplant pathology ( 24 ), and donor lymphatics may thus represent a previously underappreciated target of alloimmune responses.
|
[
{
"end": 41,
"label": "Tissue",
"start": 24,
"text": "blood vasculature"
},
{
"end": 123,
"label": "Tissue",
"start": 107,
"text": "donor lymphatics"
}
] |
Single_Cell
|
Source paper: PMC12435838
This study has several limitations.
|
[] |
Single_Cell
|
First, our 3D imaging was cross-sectional and included a small number of fixed samples, restricting inference of dynamic events during transplant rejection.
|
[] |
Single_Cell
|
Second, and common to all scRNA-Seq studies of human tissues, our control tissues were derived from nontransplanted kidneys and tumour nephrectomies and are thus likely subject to inflammatory changes.
|
[
{
"end": 60,
"label": "Tissue",
"start": 47,
"text": "human tissues"
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{
"end": 81,
"label": "Tissue",
"start": 66,
"text": "control tissues"
},
{
"end": 123,
"label": "Tissue",
"start": 100,
"text": "nontransplanted kidneys"
},
{
"end": 148,
"label": "Tissue",
"start": 128,
"text": "tumour nephrectomies"
}
] |
Single_Cell
|
We attempted to mitigate this by using samples with histological evidence of minimal chronic damage.
|
[] |
Single_Cell
|
Third, although we identified expression of coinhibitory ligands and evidence of alloantibody binding of kidney lymphatics, the downstream consequences on alloimmunity and graft function require further mechanistic study, which is challenging given the absence of an animal model that mimics the long-term sequalae of chronic mixed rejection, which occurred in our cohort of patients over decades to years, while enabling simultaneous genetic or pharmacological manipulation of LECs in a targeted manner.
|
[
{
"end": 122,
"label": "Tissue",
"start": 105,
"text": "kidney lymphatics"
},
{
"end": 482,
"label": "CellType",
"start": 478,
"text": "LECs"
}
] |
Single_Cell
|
Source paper: PMC12435838
Together, our data provide a comprehensive and multimodal view of the lymphatic vasculature in human kidney health and rejection.
|
[
{
"end": 119,
"label": "Tissue",
"start": 98,
"text": "lymphatic vasculature"
}
] |
Single_Cell
|
We propose that lymphatics acquire a tolerogenic, IFN-γ–driven phenotype during chronic rejection, but this is accompanied by structural disorganization and immune-associated perturbations.
|
[
{
"end": 26,
"label": "Tissue",
"start": 16,
"text": "lymphatics"
}
] |
Single_Cell
|
These findings point to a potentially new perspective on the role of lymphatic remodeling in transplantation, featuring a tolerogenic profile yet subject to alloimmune injury.
|
[] |
Single_Cell
|
This work lays the foundation for future studies exploring kidneys in health and disease and opens new avenues for therapeutic targeting of the lymphatic vasculature to improve the longevity of kidney transplants.
|
[
{
"end": 66,
"label": "Tissue",
"start": 59,
"text": "kidneys"
},
{
"end": 165,
"label": "Tissue",
"start": 144,
"text": "lymphatic vasculature"
},
{
"end": 212,
"label": "Tissue",
"start": 194,
"text": "kidney transplants"
}
] |
Single_Cell
|
Source paper: PMC12435838
Given the exploratory nature of 3D imaging and scRNA-Seq performed in this study and the limited kidneys available for 3D imaging analysis, sex was not considered as a biological variable.
|
[
{
"end": 132,
"label": "Tissue",
"start": 125,
"text": "kidneys"
}
] |
Single_Cell
|
Source paper: PMC12435838
Human kidney tissue was fixed in 4% paraformaldehyde in PBS at 4°C overnight and stored in PBS with 0.02% sodium azide.
|
[
{
"end": 47,
"label": "Tissue",
"start": 28,
"text": "Human kidney tissue"
}
] |
Single_Cell
|
A modified SHANEL protocol ( 128 ) was used for whole-mount immunolabeling, followed by optical clearing in benzyl alcohol/benzyl benzoate (1:2).
|
[] |
Single_Cell
|
Imaging was performed using an LSM880 upright confocal microscope (Zeiss) or custom-built mesoscale selective plane illumination microscope (mesoSPIM) ( 129 ).
|
[] |
Single_Cell
|
Image segmentation and 3D reconstruction were carried out in Imaris and Amira.
|
[] |
Single_Cell
|
Source paper: PMC12435838
where x 1 and x 2 are the 2 closest adjacent nodes from the lymphatic 3D skeleton, found by minimizing cross–nearest neighbor distances, and x 0 is the centroid of the cell of interest.
|
[
{
"end": 78,
"label": "Tissue",
"start": 73,
"text": "nodes"
},
{
"end": 109,
"label": "Tissue",
"start": 88,
"text": "lymphatic 3D skeleton"
},
{
"end": 212,
"label": "CellType",
"start": 196,
"text": "cell of interest"
}
] |
Single_Cell
|
To evaluate whether the cell distances were different from what would be expected by chance, within each region of interest, the CD4 T cell and CD20 B cell populations were randomly redistributed under complete spatial randomness for 20 simulations.
|
[
{
"end": 139,
"label": "CellType",
"start": 129,
"text": "CD4 T cell"
},
{
"end": 167,
"label": "CellType",
"start": 144,
"text": "CD20 B cell populations"
}
] |
Single_Cell
|
A comparison was then made as to whether the measured mean cell-lymphatic distances fell within the 95% CIs obtained through the simulations under complete spatial randomness.
|
[] |
Single_Cell
|
Source paper: PMC12435838
Single-cell suspensions from fresh kidney explants were processed using the 10x Genomics Chromium 5′v2 kit and sequenced on an Illumina NovaSeq.
|
[
{
"end": 78,
"label": "Tissue",
"start": 63,
"text": "kidney explants"
}
] |
Single_Cell
|
Data were mapped to GRCh38 and processed using Scanpy and Seurat, using scVI ( 132 ) or Harmony ( 133 ) for integration.
|
[] |
Single_Cell
|
Cell identity was assigned via marker gene expression and assisted by CellTypist prediction.
|
[] |
Single_Cell
|
Differential expression was assessed using Wilcoxon rank-sum tests and GO term enrichment using PANTHER.
|
[] |
Single_Cell
|
To infer putative cell-cell interactions in scRNA-Seq data, the CellPhoneDB resource ( 85 ) was used.
|
[] |
Single_Cell
|
To generate the human lymphatic cell atlas, LECs were extracted from publicly available single-cell datasets across multiple organs and integrated using Harmony.
|
[
{
"end": 48,
"label": "CellType",
"start": 44,
"text": "LECs"
},
{
"end": 131,
"label": "Tissue",
"start": 125,
"text": "organs"
}
] |
Single_Cell
|
SCENIC ( 134 ) was used to infer transcription factor activity across clusters.
|
[] |
Single_Cell
|
The NephroSeq database (v5, RRID:SCR_019050) was used to examine candidate genes by pulling data from its online browser.
|
[] |
Single_Cell
|
Source paper: PMC12435838
Adult human dermal LECs (PromoCell, C-12217) were cultured in MV2 medium and treated with recombinant human IFN-γ (50 ng/mL) or unstimulated control medium for 24, 48, or 72 hours.
|
[
{
"end": 51,
"label": "CellType",
"start": 28,
"text": "Adult human dermal LECs"
},
{
"end": 71,
"label": "CellLine",
"start": 64,
"text": "C-12217"
}
] |
Single_Cell
|
LGALS9 transcript levels were quantified by qRT-PCR and normalized to HPRT using the 2 method.
|
[] |
Single_Cell
|
Secreted LGALS9 protein in conditioned media was measured by ELISA (R&D Systems).
|
[] |
Single_Cell
|
Data are shown as fold-change relative to untreated controls.
|
[] |
Single_Cell
|
Assays were performed across 2 independent cell lines in triplicate.
|
[
{
"end": 53,
"label": "CellLine",
"start": 31,
"text": "independent cell lines"
}
] |
Single_Cell
|
Source paper: PMC12435838
Use of human tissue was approved by NHS Blood & Transplant (NHSBT), the National Research Ethics Committee in the UK (21/WA/0388, NC.2018.010, NC.2018.007, REC 16/EE/0014), and the Royal Free London NHS Foundation Trust-UCL Biobank Ethical Review Committee (RFL B-ERC/B-ERC-RF, NC.2018.010; IRAS 208955).
|
[
{
"end": 47,
"label": "Tissue",
"start": 35,
"text": "human tissue"
}
] |
Single_Cell
|
Written informed consent for research use of donated organs was obtained via NHSBT.
|
[
{
"end": 59,
"label": "Tissue",
"start": 45,
"text": "donated organs"
}
] |
Single_Cell
|
Ethical approvals for public datasets are detailed in the original studies.
|
[] |
Single_Cell
|
Source paper: PMC12435838
Human neural organoids, generated from pluripotent stem cells in vitro, are useful tools to study human brain development, evolution and disease.
|
[
{
"end": 89,
"label": "CellType",
"start": 67,
"text": "pluripotent stem cells"
}
] |
Single_Cell
|
However, it is unclear which parts of the human brain are covered by existing protocols, and it has been difficult to quantitatively assess organoid variation and fidelity.
|
[
{
"end": 53,
"label": "Tissue",
"start": 42,
"text": "human brain"
}
] |
Single_Cell
|
Here we integrate 36 single-cell transcriptomic datasets spanning 26 protocols into one integrated human neural organoid cell atlas totalling more than 1.7 million cells .
|
[
{
"end": 169,
"label": "CellType",
"start": 164,
"text": "cells"
}
] |
Single_Cell
|
Mapping to developing human brain references shows primary cell types and states that have been generated in vitro, and estimates transcriptomic similarity between primary and organoid counterparts across protocols.
|
[
{
"end": 69,
"label": "CellType",
"start": 51,
"text": "primary cell types"
}
] |
Single_Cell
|
We provide a programmatic interface to browse the atlas and query new datasets, and showcase the power of the atlas to annotate organoid cell types and evaluate new organoid protocols.
|
[
{
"end": 147,
"label": "CellType",
"start": 128,
"text": "organoid cell types"
}
] |
Single_Cell
|
Finally, we show that the atlas can be used as a diverse control cohort to annotate and compare organoid models of neural disease, identifying genes and pathways that may underlie pathological mechanisms with the neural models.
|
[] |
Single_Cell
|
The human neural organoid cell atlas will be useful to assess organoid fidelity, characterize perturbed and diseased states and facilitate protocol development.
|
[] |
Single_Cell
|
Source paper: PMC11578878
Human neural organoids, self-organizing three-dimensional human neural tissues grown in vitro, are becoming powerful tools for studying the mechanisms of human brain development, evolution and disease .
|
[
{
"end": 106,
"label": "Tissue",
"start": 52,
"text": "self-organizing three-dimensional human neural tissues"
}
] |
Single_Cell
|
They can be generated using external patterning factors (for example, morphogens) to guide their development towards certain brain regions or to drive the emergence of specific cell types (guided protocols) .
|
[] |
Single_Cell
|
Conversely, unguided protocols rely on the self-patterning capacity of organoids to generate diverse cell types and states .
|
[
{
"end": 111,
"label": "CellType",
"start": 93,
"text": "diverse cell types"
}
] |
Single_Cell
|
Source paper: PMC11578878
Single-cell RNA sequencing (scRNA-seq) is a powerful technology to characterize cell type heterogeneity in complex tissues, and has illuminated a remarkable heterogeneity of diverse progenitor, neuronal and glial cell types that can develop within neural organoids , as well as differentiation trajectories of certain neural lineages.
|
[
{
"end": 150,
"label": "Tissue",
"start": 135,
"text": "complex tissues"
},
{
"end": 220,
"label": "CellType",
"start": 210,
"text": "progenitor"
},
{
"end": 230,
"label": "CellType",
"start": 222,
"text": "neuronal"
},
{
"end": 251,
"label": "CellType",
"start": 235,
"text": "glial cell types"
},
{
"end": 292,
"label": "Tissue",
"start": 276,
"text": "neural organoids"
},
{
"end": 361,
"label": "CellType",
"start": 338,
"text": "certain neural lineages"
}
] |
Single_Cell
|
The data also enable the comparison of human neural organoid cells to those in the primary human brain, and most analyses have revealed strong similarity in molecular signatures .
|
[
{
"end": 66,
"label": "CellType",
"start": 39,
"text": "human neural organoid cells"
},
{
"end": 102,
"label": "Tissue",
"start": 83,
"text": "primary human brain"
}
] |
Single_Cell
|
Substantial differences have also been reported, including differential gene expression linked to media components and perturbed metabolic signatures associated with glycolysis .
|
[] |
Single_Cell
|
Nevertheless, analysis of organoid tissues supports a useful recapitulation of early brain development, and scRNA-seq methods have been applied to study the molecular basis of neural cell type fate determination , evolutionary differences in primates and pathological changes in neural disorders .
|
[
{
"end": 42,
"label": "Tissue",
"start": 26,
"text": "organoid tissues"
}
] |
Single_Cell
|
However, it is unclear which portions of the developing central nervous system can be generated with existing protocols and which ones are still lacking.
|
[
{
"end": 78,
"label": "Tissue",
"start": 45,
"text": "developing central nervous system"
}
] |
Single_Cell
|
It has also remained challenging to systematically quantify the transcriptomic fidelity of neural organoid cells compared to their primary counterparts.
|
[
{
"end": 112,
"label": "CellType",
"start": 91,
"text": "neural organoid cells"
}
] |
Single_Cell
|
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