Microbial carbon use efficiency (CUE), the ratio of C retained in biomass to C assimilated by microbes, is central to our understanding of organic C turnover in soil. To unravel the factors that control CUE and to decipher patterns in C utilization and metabolic activity, the use of soil-free microbial cell extract (SFCE) seems promising. We propose that by isolating active microbial cells from soil, the amount of C taken up by specific populations and their efficiency of C utilization can be more precisely quantified in short-term incubations, than while facing the complex background of the soil matrix.

Therefore, we have revisited and optimized established protocols to extract microbial cells from agricultural soil via Nycodenz density gradients. The extracted cells were counted via fluorescent live-dead staining and accounted for up to ~25% of the original soil biomass. We then used calorespirometric measurements (metabolic heat and respiration) to compare CUE values of SFCE and intact soil under-provisioning of different substrates (glycerol, glucose, glutamine, and citric acid). Respiration data was collected for 24 and 48h, whereas metabolic heat was continuously measured.

A substantial fraction of viable microbial cells were extracted from the soil using Nycodenz, with numbers ranging from 10⁷ to 10⁸ per gram of soil. The CUE values calculated from calorespirometric ratios suggested that SFCE had a relatively higher per-cell CUE than intact soil during the initial 24h of incubation. Substrate-specific distinctions in heat production between both approaches were clearly apparent. Prokaryotic communities in soil and SFCE before and after incubation were analyzed via amplicon sequencing, to identify the taxa most responsive to substrate addition. The number of significantly enriched taxa in soil and SFCE compared to control samples were glycerol (0, 16), glucose (6, 41), glutamine (2, 46), and citric acid (1, 38), respectively.

The data generated from studies using SFCE provides a valuable new handle to refine models which are needed to predict how changes in environmental conditions, or climate scenarios may impact microbial CUE and C cycling in soils.