Other, potentially more time-consuming approaches for more direct quantification of microbial abundance include flow cytometric counting
of cells in combination with sequencing. Previously conducted using
direct plate or cell counts, flow cytometry is relatively higher
throughput and more accurate. In this approach, cells are separated from
large soil particles and incubated with nucleic acid stain prior to flow
cytometric enumeration. Counting of cells using flow cytometry may
circumvent overestimation of microbial diversity related to
extracellular DNA by counting only intact cells
(48, 49).
Additionally, fluorescence microscopy using common nucleic acid stains
(e.g. DAPI, Sybr-green) can be applied to achieve direct cell counts. However,
this approach is relatively lower throughput and less accurate than flow
cytometric enumeration
(45).
An additional approach to improve the quantitative nature of amplicon
sequencing is Catalyzed Reporter Deposition coupled to Fluorescence in
situ hybridization (CARD-FISH). Recently, Piwosz and colleagues combined
CARD-FISH with sequencing
(50). Although the
use of CARD-FISH is more labor-intensive than amplicon sequencing alone,
it allows sequencing to become more quantitative through direct cell
counts and phylogenetic staining of microorganisms of interest. This
approach is restricted to the analysis of bacteria and archaea, as
high-throughput sequencing data and CARD-FISH analysis of eukaryotic
organisms correlate poorly with one another
(50). The
correlation between datasets was higher for bacteria and taxa that were
highly underrepresented in the sequencing data could be captured using
CARD-FISH. Some of these approaches remain limited at present due to the
sheer biological diversity of soil ecosystems. However, with an
expanding view of the diversity of microorganisms and growing number of
published reference genomes, more accurate quantitative approaches are
within reach. In combining sequencing with quantitative measurements,
one can obtain absolute abundances of organisms in a given sample,
making investigations of complex microbial communities more robust.