A Nanopathology Platform for Prediction and Early Detection of Disease in Kidney Transplant Rejection
Lead Research Organisation:
The Francis Crick Institute
Department Name: Research
Abstract
Chronic kidney failure affects about 11% of the world population. Diseases caused by the immune system are the third most common cause of chronic kidney failure. Transplantation is the best available treatment for patients who reach end stage kidney failure, but sadly a transplanted kidney does not last for more than 10-15 years on average, mainly because the recipient of the transplant mounts an immune reaction against the donated kidney, called transplant rejection. In particular, if the recipient of the transplant develops antibodies against the donor tissue, the outcomes are poor. This is referred to as "antibody mediated rejection". Current treatments for antibody-mediated rejection mainly involve removing or blocking the effect of the antibody, but these are not very effective. We need to better understand rejection in order to develop new and better treatments.
Our current understanding of rejection comes from examining biopsies of the transplant using a standard light microscope, and from analysing expression of genes in biopsy tissue. Antibodies and other molecules gather in the small blood vessels of the kidney, and attract a variety of immune cells. These immune cells get 'activated' and damage the lining of the blood vessels, causing thickening of the vessel walls. Eventually, this prevents the kidney from filtering fluid and waste from the blood. There is still a lot we don't know about which immune cells are responsible, how they get activated and what effect this has on the lining of the blood vessels. In part this is due to the difficulties in imaging the molecules and cells within the small blood vessels in human biopsies taken for diagnosis, both at high resolution and in large tissue volumes. The great challenge in imaging large pieces of tissue at high resolution is the time it takes. To image a biopsy of 10 mm length and 1 mm diameter at the resolution required to identify individual immune cells and to see them attacking the vessel walls would take 30-55 years for one sample. This is clearly impractical.
To overcome this problem, we will develop a suite of new imaging techniques and combine them, so that we can follow vessels and map all of the immune cells in a biopsy. We will then zoom in on each cell, analysing proteins and genes to produce a barcode that confirms what type of immune cell it is and whether it is active in damaging the vessels. High resolution images will also help us to assess whether each cell is in 'attack mode'. To do this, we will use the latest imaging technology available to us, and adapt each technique to make them work together across scales, on tissue left over after diagnosis is complete in human biopsies. We will harness the power of X-rays at the Petra III synchrotron in Germany, and cutting edge light and electron microscopes at the Francis Crick Institute in London and the European Molecular Biology Laboratory. Using this new 'multimodal multiscale' approach, we will reduce the time taken to analyse a human biopsy from 55 years to 5 days.
At this speed, we will be able to amass data from enough biopsies that we can start to look for patterns in the recruitment of antibodies and immune cells to the kidneys of patients with transplant rejection, at early and late stages. We will analyse the abundant data using the latest methods in artificial intelligence. Our grand aim is to be able to map successive stages in the immune reaction to the kidney transplant, throughout each biopsy sample, down to the nanoscale, and ultimately to use this information to predict which transplants will fail and to inform the development of new treatments of organ rejection.
Our current understanding of rejection comes from examining biopsies of the transplant using a standard light microscope, and from analysing expression of genes in biopsy tissue. Antibodies and other molecules gather in the small blood vessels of the kidney, and attract a variety of immune cells. These immune cells get 'activated' and damage the lining of the blood vessels, causing thickening of the vessel walls. Eventually, this prevents the kidney from filtering fluid and waste from the blood. There is still a lot we don't know about which immune cells are responsible, how they get activated and what effect this has on the lining of the blood vessels. In part this is due to the difficulties in imaging the molecules and cells within the small blood vessels in human biopsies taken for diagnosis, both at high resolution and in large tissue volumes. The great challenge in imaging large pieces of tissue at high resolution is the time it takes. To image a biopsy of 10 mm length and 1 mm diameter at the resolution required to identify individual immune cells and to see them attacking the vessel walls would take 30-55 years for one sample. This is clearly impractical.
To overcome this problem, we will develop a suite of new imaging techniques and combine them, so that we can follow vessels and map all of the immune cells in a biopsy. We will then zoom in on each cell, analysing proteins and genes to produce a barcode that confirms what type of immune cell it is and whether it is active in damaging the vessels. High resolution images will also help us to assess whether each cell is in 'attack mode'. To do this, we will use the latest imaging technology available to us, and adapt each technique to make them work together across scales, on tissue left over after diagnosis is complete in human biopsies. We will harness the power of X-rays at the Petra III synchrotron in Germany, and cutting edge light and electron microscopes at the Francis Crick Institute in London and the European Molecular Biology Laboratory. Using this new 'multimodal multiscale' approach, we will reduce the time taken to analyse a human biopsy from 55 years to 5 days.
At this speed, we will be able to amass data from enough biopsies that we can start to look for patterns in the recruitment of antibodies and immune cells to the kidneys of patients with transplant rejection, at early and late stages. We will analyse the abundant data using the latest methods in artificial intelligence. Our grand aim is to be able to map successive stages in the immune reaction to the kidney transplant, throughout each biopsy sample, down to the nanoscale, and ultimately to use this information to predict which transplants will fail and to inform the development of new treatments of organ rejection.
Technical Summary
Chronic kidney failure affects about 11% of the world population, of which one third of cases involve immune-mediated disease. Those patients who reach end stage kidney failure require renal replacement therapy with dialysis or transplantation. Dialysis is associated with poor outcomes, and though transplantation considerably improves quality of life, the longevity of transplant organs is limited, mainly due to immune rejection.
The current accepted pathophysiological model of antibody-mediated injury derives from low resolution imaging and gene expression studies on human biopsy material, and on live imaging of animal models. This model shows that antibodies and complement fragments are deposited in the kidney, which recruits innate and adaptive immune cells, which in turn cause injury to the kidney cells leading to cellular changes, matrix accumulation and loss of kidney function.
Understanding which immune cells are important in the early response to antibodies, and the mechanisms by which they cause injury, underpins development of effective future therapies. However, to do this, we need to develop better microscopy methods that are capable of imaging all immune cells within human biopsies. The resolution required to image subcellular features of immune cell activation is in the order of 10 nm. For a biopsy of 10 mm length and 1 mm diameter, it would take around 55 years to image.
We will develop a multimodal multiscale imaging and spatial analysis pipeline, which will allow us to visualise all blood vessels and immune cells within a biopsy, whilst incorporating ultrastructural features, surface markers and gene expression to unequivocally assign immune cell IDs and activation states. We will reduce the time taken to analyse a single human biopsy from 55 years to 5 days, so that we can start to look for patterns in the data using supervised and unsupervised machine learning, and create a pseudotime model of disease progression at the nanoscale.
The current accepted pathophysiological model of antibody-mediated injury derives from low resolution imaging and gene expression studies on human biopsy material, and on live imaging of animal models. This model shows that antibodies and complement fragments are deposited in the kidney, which recruits innate and adaptive immune cells, which in turn cause injury to the kidney cells leading to cellular changes, matrix accumulation and loss of kidney function.
Understanding which immune cells are important in the early response to antibodies, and the mechanisms by which they cause injury, underpins development of effective future therapies. However, to do this, we need to develop better microscopy methods that are capable of imaging all immune cells within human biopsies. The resolution required to image subcellular features of immune cell activation is in the order of 10 nm. For a biopsy of 10 mm length and 1 mm diameter, it would take around 55 years to image.
We will develop a multimodal multiscale imaging and spatial analysis pipeline, which will allow us to visualise all blood vessels and immune cells within a biopsy, whilst incorporating ultrastructural features, surface markers and gene expression to unequivocally assign immune cell IDs and activation states. We will reduce the time taken to analyse a single human biopsy from 55 years to 5 days, so that we can start to look for patterns in the data using supervised and unsupervised machine learning, and create a pseudotime model of disease progression at the nanoscale.
