However, capturing a full cell or nucleus can be problematic [21]. Solid tumours also shed cells in a patient’s blood stream (circulating tumour cells or CTCs) and cells disseminating to distant organs (disseminated tumour cells or DTCs) (Figure 1). DTCs can remain dormant over a prolonged period of time following resection of the primary tumour, before giving rise to overt metastases [22]. Investigating CTCs and DTCs is important not only for understanding tumour evolution and progression, but also as 17-AAG cell line liquid

biopsies of a solid tumour for guiding diagnosis, prognosis and treatment. Although often just a few CTCs in millilitres of peripheral blood of a cancer patient are present, various isolation techniques based on physical and biological properties of CTCs have been described [23, 24• and 25]. However, a main difficulty remains that unbiased CTC-isolation requires the definition of suitable biomarkers that are expressed in all blood-borne tumour this website cells, but not in normal circulating cells. Similarly, defined physical and biological properties of DTCs, commonly homing to the bone marrow, can be used for their isolation following needle aspiration

through the iliac crest [23 and 24•]. Modern genomics technologies require hundreds of nanograms of input material, while a normal diploid human cell contains about 7 pg of DNA. Hence, whole-genome amplification (WGA) is required to enable analysis of a single cell. WGA of single-cell DNA is based on Multiple Displacement Amplification (MDA), Polymerase Chain Reaction (PCR), or a combination of principles of both displacement amplification and PCR (Figure 2). Importantly, all amplification methods suffer from various imperfections that hamper straightforward

reliable identification of genetic variation. The breadth of genomic coverage, amplification biases (due to local differences in %GC-content or other factors), the prevalence of chimeric DNA molecules, allelic drop outs (ADO), preferential allelic amplifications (PA) and nucleotide copy errors can differ significantly between different WGA approaches. As such, some methods are more apt than others to detect specific genetic variants GBA3 [26••, 27••, 28 and 29]. In theory, massively parallel sequencing allows profiling the full spectrum of genetic variation in a cell’s WGA product, from ploidy changes to aneuploidy and (un)balanced structural variants, down to indels and base substitutions. However, the various confounding factors of WGA complicate this process (Figure 3). A one-fit-all WGA method remains to be established, and a comparative analysis of all WGA methods against a benchmark case is acutely needed, assaying the potency of genetic variation detection, including examining the favourable effects of the reduction of reaction volumes and amplification cycles [30••].