New Preprint 📢 from our team 🔎 Critical assessment of #intratumor and #low #biomass #microbiome using #longread sequencing Have you heard about #cancer microbiome or #intratumor microbiome? Some studies suggest bacteria 🦠 live inside tumors and influence cancer treatment. But there’s also been a major #debate: in these very​ low-microbe tissue samples, how much of the microbial signal is real ❓ and how much is background noise from the lab or the environment ❓ We tackle this with a simple idea: look at DNA fragment sizes using #longread sequencing. If microbes are truly present as intact cells, their DNA should show up as long, genome-like fragment distributions, not mostly short broken (degraded) pieces. Long-read data lets us distinguish those two cases! To make this more robust across datasets, we developed a #metric that normalizes microbial DNA fragment lengths to human DNA fragment lengths within the same sample. Negative #controls: germ-free mice and human cell culture datasets, where true resident microbes are not expected and any microbial signal is most likely introduced during workflows (i.e. contaminations). Positive #controls: bacteria spike-in, and importantly, GI tumors with well established bacteria: e.g. #Helicobacter #pylori in #gastric cancer, #Fusobacterium in #colorectal cancer. Across multiple tumor types and tissues (public + new data), we find that long microbial reads are mainly seen in tissues with natural microbial exposure (such as #gut, #stomach, #skin, #vagina and #cervix, etc). Outside those settings, most microbial signals look more like fragmented, and the occasional long fragments often match well-known #contaminant organisms. One finding that stood out to us was the #lung, which is constantly exposed to microbes we breathe in, yet in our analysis, the microbial signal appeared mostly as short, fragmented DNA, which is more consistent with transient exposure and rapid clearance than with stable resident microbes in deep lung tissue. The new metric also helps #unify earlier debates about microbiomes reported in #placenta and #blood. When we reanalyzed long-read data from those settings using the same read length-based approach, we found no evidence for a stable resident microbiome under normal conditions, consistent with the current consensus that most signals reflect background contamination, with true positives mainly expected during active infection. Long story short, but this was a 4+ years effort led by Yanchun Zhang and Andy Mead. Grateful to all collaborators who contributed valuable samples and feedback: Mi Ni, @MagdalenaKsi, @clannabel7, @LauraZuluagaJim, Gintaras Deikus, Robert Sebra, Rachel Brody, Raymund Yong, @NYCRoboticTeam, @Xue_Song__Zhang. @SinaiGenetics @IcahnInstitute Link: biorxiv.org/content/10.64898… We’d love your thoughts! #longread @PacBio @nanopore #metagenomics #pacbio #nanopore