A, Diagnosis of SRBCTs using mRNA expression profiling and artificial neural networks (ANNs). Yellow, Ewing’s sarcoma; Green, neuroblastoma; Red, rhabdomyosarcoma; Blue, Burkett’s lymphoma; Black, non-SRBCT test samples. a, Hierarchical clustering of 88 SRBCT samples using the 96-clone profiles. *, genes that have not been reported to be associated with these cancers. b, The training samples are shown in a multidimensional scaling analysis using the top 96 ANN-ranked cDNA clones. The samples cluster closely according to the 4 different cancer categories. c, Classification and diagnosis of the samples. A sample is classified to a cancer category according to its highest committee vote (average of all ANN outputs) and placed in the corresponding plot. The distance between its committee vote and the ideal vote (for example, for EWS, it is EWS =1, RMS = NB = BL = 0) for each sample is plotted for that diagnostic category. Vertical dashed lines represent the boundary where 95% of the training samples are included. Training samples are represented as squares and test samples as triangles. Non-SRBCT samples are colored black. All SRBCT samples, including the 20 tests, were correctly classified. The diagnosis of all 5 non-SRBCT test samples was rejected since they lie outside the dashed lines. Three of the SRBCT samples (EWS-T13, TEST-10 and TEST-20) though correctly classified could not be confidently diagnosed. Adapted by permission from Macmillan Publishers Ltd: Nature Medicine, [16], copyright 2001.
B, Hierarchical clustering of tumors using miRNA expression profiles. a, miRNA profiles of 218 samples from several different tissues were clustered. Samples are in columns, miRNAs in rows. EP, samples of epithelial origin; GI, samples from the gastrointestinal tract. b, Clustering of 73 bone marrow samples from patients with acute lymphoblastic leukemia (ALL). Colored bars indicate the different ALL subtypes. c, Comparison of miRNA and mRNA profiles. For 89 epithelial samples from a that had mRNA expression data, hierarchical clustering was performed. Samples of GI origin are shown in blue. GI-derived samples largely cluster together when using miRNA expression profiles, but not when mRNA expression profiles are used. Bldr, bladder; Brst, breast; Fcc, follicular lymphoma; Kid, kidney; Lvr, liver; Mela, melanoma; Meso, mesothelioma; Pan, pancreas; Prost, prostate; Stom: stomach; Ut, uterus; AML: acute myelogenous leukaemia; BALL, B-cell ALL; LBL, diffuse large-B cell lymphoma; MF, mycosis fungoides; MLL, mixed lineage leukaemia; TALL, T-cell ALL; Hyper 47-50, hyperdiploid with 47-50 chromosomes; Hyper.50, hyperdiploid with over 50 chromosomes; Normp, normal ploidy. Adapted by permission from Macmillan Publishers Ltd: Nature, [32], copyright 2005.

A, micro plastic beads are color-coded recognizable by a red laser beam. B, Probes, nucleic acids or antibodies, are conjugated to specific beads. C, Beads with different probes are mixed and hybridized with samples labeled with a fluorescent reporter that can be detected by a green laser beam. D. Flow cytometry is performed with beads, where the molecules are identified by the color code of the beads and quantified by the fluorescent reporter. E, Expression profiles can be obtained for a hundred of molecules in one test tube.

A, Meso scale discovery (MSD) technology is a multiplex assay where the levels of multiple proteins can be determined using electrochemiluminescence. This immunoassay uses capture antibodies coated on the surface of plates to capture proteins from whole cell lysates, which can be then detected by an antibody labeled with an electrochemiluminescent tag [47].
B, Isotope-coded affinity tag (ICAT) uses heavy [d(8)] and light [d(0)] labeling reagents to tag protein samples of two states (e.g. diseased and normal). The samples are combined, digested with trypsin into peptides, purified with the affinity tags, separated with multidimensional chromatography, and then analyzed by MS and MS/MS for quantification and identification respectively. The relative abundances of labeled peptides are determined by comparison of peak intensities between the light and heavy forms of the peptides, which are separated by 8 Da. Reprinted with permission from the Annual Review of Biochemistry, Volume 72 ©2003 by Annual Reviews [54,55].
C, Electronic hybridization sensing using field effect transistors (FET). a, principals of a DNA sensing device using FET. In the absence of charge at the gate, no current will flow at any voltage so that the transconductance curve traces a horizontal line (left). Single-stranded DNA oligonucleotides (probe DNA) immobilized at the gate cause a net negative charge from their phosphate backbone. The resulting transconductance curve will exhibit slight but measurable departure from horizontal linearity (middle). If the single-stranded DNA probes were hybridized with a complementary sequence, a net doubling of charge will occur at the gate. This will drive the transistor into forward biased full operation, and will produce large amplitude and highly nonlinear transconductance curves (right). b, Transconductance curves of a FET device (similar to the device on the right panel) demonstrate its ability to distinguish specific DNA hybridization from non-specific DNA interaction. The green arrow represents the change of conductance of the FET from the empty probe state to specific DNA-hybridization state. Empty probe, single-strand DNA probe is immobilized; non-specific, incubated with DNA of unmatched sequence; Specific, hybridized with complementary DNA. c, an example of a carbon nanotube (CNT) transistor array on a microchip. CNTs bridge the gap between source (S) and drain (D) electrodes. G is the gate electrode in contact with the hybridization solution.

A, Prediction by mRNA profiles. Identification of a 19-gene signature that can predict the outcome of neuroblastoma patients. a, Prognostic results from artificial neural networks (ANN) for patients using the 19-gene signature, where alive=0 and deceased=1. ANNs were trained using 35 training samples, and then were used to predict the outcome of 21 independent test samples. Horizontal dotted line divides the test (above the line) from the training samples. b and c, Kaplan-Meier curves for survival probability of the all patients using the 19 gene signature (b) or current COG risk-stratification (c). Prognostic prediction using the 19 gene signature is favorable over the current COG risk-stratification. d, A Kaplan-Meier curve for 13 high-risk patients without MYCN amplification were derived from the ANN prediction using the 19 gene signature. Figures are taken from Wei et. al. [17] with permission.
B, Prediction by DNA profiles. Whole chromosomal number changes (WCC) predict outcome in neuroblastoma patients in Kaplan-Meier survival analyses. The survival probabilities of patients with WCC ≥2 or WCC<2 are compared in all samples with survival information (a, n=51). The patients classified using the WCC status had significantly different survival probabilities (P= 0.01). b and c, The patients diagnosed before or in 1998 (b, n=23), and after 1998 (c, n=27). d, The patients diagnosed after 1998 with age > 500 days (n=15). Figures are taken from Bilke et. al. [43] with permission.

A, A schematic diagram for multiplexed RT-PCR assay. a, RNA samples are first reverse transcribed and partially amplified using a pair of chimeric primers, and then further amplified using a pair of universal primers. b, PCR products are separated using fluorescence capillary electrophoresis. A gene is identified by the size of its amplicon, and its expression level is represented by the peak area in a chromatogram. c, Four representative chromatograms for four different categories of tumor samples from one multiplex RT-PCR assay.
B, The three-dimensional plot clearly demonstrated that 31 top-ranked genes can accurately classify 4 different categories of SRBCTs in a multidimensional scaling analysis. Blue, lymphoma; yellow, Ewing’s sarcoma; red, rhabdomyosarcoma; and green, neuroblastoma.
C, Hierarchical clustering of all 96 SRBCT samples using 31 top-ranked genes after log2 transformation and z-scoring across all samples.
Figures are taken from Chen et. al. [46], J. Mol. Diagn., 2007, 9:80-88 with permission from the American Society for Investigative Pathology and the Association for Molecular Pathology.