Gene expression analysis reveals diabetes-related gene signatures

Table 1 Characterization of the studies selected for bioinformatic analysis

Study	Dataset	Human pancreatic islets samples		Sample isolation method	Protocol design
Study	Dataset	Total n°	Type	Sample isolation method	Protocol design
Doi: https://doi.org/10.1038/s42255-021-00420-9	GSE164416	57	18 ND, 39 T2D	Laser capture microdissection	SMART-seq Illumina Hiseq 2500 or 500
Doi: https://doi.org/10.1074/jbc.M110.200295	GSE25724	13	7 ND, 6 T2D	Collagenase digestion followed by density gradient purification	HG- U133A Affymetrix chips
Doi: https://doi.org/10.1371/journal.pone.0011499	GSE20966	20	10 ND, 10 T2D	Laser capture microdissection	DNA-Chip Analyzer
Doi: https://doi.org/10.1007/s00125-017-4500-3	GSE76894	103	84 ND, 19 T2D	Enzymatic digestion or laser capture microdissection	Affymetrix Human Genome U133 Plus 2.0 Array
Doi: https://doi.org/10.1101/gr.212720.116	GSE86473	8	5 ND, 3 T2D	Cultured and isolated via C1 integrated fluidic circuit	SMART-seq2 Illumina NextSeq500
Doi: https://doi.org/10.1016/j.cmet.2020.04.005	GSE124742	6	3 ND, 3 T1D	Patch-seq and FACS	SMART-seq2 Illumina NextSeq500 or Novaseq platform

ND individuals without diabetes, T2D individuals with type 2 diabetes, T1D individuals with type 1 diabetes, FACS fluorescence-activating cell sorting

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