Current models of the inflammatory myopathies have largely come from the use of microscopy to study muscle tissue. We have used analogous approaches to “look at muscle tissue” without preconceived ideas as to what we will see. Like the microscope, these technologies – microarrays and mass spectrometry based proteomics – are superb at opening new opportunities for understanding the inflammatory myopathies.
Our studies are almost entirely on human tissue and blood samples from patients with inflammatory myopathies. We have a large group of patients (over 350 as of Sept 1, 2006) that have enrolled in one of our research studies by providing a small extra piece of muscle tissue during a clinically indicated diagnostic muscle biopsy. We have additionally collected over 100 blood samples. Most of these specimens are from patients with inflammatory myopathies.
We study these samples through careful and large-scale observation, resulting in the accumulation of parallel data sets – clinical data, microscopic pathological data, microarray data, selected gene sequence data, and proteomics data. Some of this data is available publicly in National Institutes of Health databases, with links on this web site, and some of it in internally linked databases that provide an engine for rapid discovery for our research group. An estimate of the types of studies and amount of data we have generated is available here.
The principal technologies we use to study muscle biopsy specimens and blood samples are:
- Microscopy, including histochemistry, immunohistochemistry, immunofluorescence histochemistry, and optical sectioning microscopy techniques.
- Microarray studies, involving the simultaneous measurement of 15,000 – 33,000 RNA transcripts as a reflection of gene expression, within tissues
- Laser capture microdissection which serves as a bridge between traditional microscopic pathological methods, and molecular methods of DNA, RNA, and protein analysis
- Mass spectrometry based proteomic methods, and more standard protein techniques
- Bioinformatic methods that provide detection of relationships among differing sources of data and statistical approaches for identifying the effects of multiple gene expression pathways involved in diseased tissue.