Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
• Data Files
• Related Publications
• Parameters
• Instruments
• Project Information
• Funding

Sample Collection. Sample water was collected from the microcosms every couple of days during the experiment incubation for enumeration of the microbial community via epiflorescent microscopy. The water was sampled from the microcosms using a 25-mL serological pipette (VWR) with the tip snapped off to widen the opening. The serological pipette was rinsed three times with Milli-Q water before drawing sample water from the center of the microcosm near the mixer.  A new serological pipette was used for each treatment. To a 50-mL falcon tube, 25 mL of sample was collected. 

Staining Procedure. For Ecocosm I (Fall 2021) 10 uL of 100x SYBR Green I was added to duplicate eppendorf tubes with 1 mL of sample water for a final stain concentration of 10x and vortexed gently for 5 seconds. The sample was incubated in the dark at room temperature for 45 minutes. For Ecocosm II, DAPI stain was used to allow for cell-sizing analysis in addition to enumeration; 50 uL of 50x DAPI solution was added to 1 mL of sample as above and incubated in the dark for 10 minutes prior to slide preparation.

Slide Preparation. Samples were filtered down onto black, 25-mm filters (GTBP, Millipore Sigma, 0.22 um) with a backing filter (PVDF, Millipore Sigma, 25-mm, 0.45 um) on a 25-mm multifilter manifold (Millipore). Each filter well was filled with c.a. 5 mL of Milli-Q water then 1 mL of stained sample was added to a filtering well and filtered down under gentle (5-10 in Hg) vacuum pressure. The GTBP filters were mounted to slides with microscopy oil and a cover slip.

Imaging. The slides were imaged on a Nikon Ti2 inverted microscope using the NIS Elements software. SYBR-stained slides were imaged at 60x magnification at 470 nm and 620 nm excitation to capture autofluorescence. DAPI-stained slides were similarly counted at 60x under 395 nm and 620 nm excitation. Manual counts calculated from 10 fields of view are reported in this dataset. Further details on imaging techniques, analysis, and automated image analysis are found in Parker (2022) in the publications section. 

List of equipment used

25-mL serological pipette for sampling (VWR, sterile)

50-mL Falcon tube (Corning)

1.7-mL eppendorf tubes (Eppendorf)

SYBR Green I in DMSO stain, 10,000x (Invitrogen): diluted to 100x (flow cytometry) with Milli-Q water, 0.2-um filtered, and frozen in 0.2 mL aliquots to prevent freeze-thaw cycles.

DAPI, 1 mL (1 mg/mL) 4',5-diamidina-2-phenylindole (Fisher Scientific)

25-mm PVDF filter (Millipore Sigma)

25-mm, 0.22 um GTBP filter (Millipore Sigma)

Filter manifold (Millipore Sigma 1225: 12, 25-mm wells)

1mm glass microscope slide (Vista-Vision)

Glass cover slips  No. 1.5 (Slip-Rite)

Microscopy immersion oil 

Nikon Ti2 inverted microscope with excitation filters for: DAPI (395/25 nm), SYBR (470/40 nm), and autofloresence (620/60 nm)

Nikon Elements Acquisition Software

The number of cells counted across 10 fields of view at 60x magnification for each sample was averaged, multiplied by the dilution factor, and corrected for the percent of filter area counted. The results in this datafile include the 60x SYBR, DAPI, and autofluorescent manual microscopy counts reported in cells/L with and standard deviation for each microcosm. Additionally, the ratio of stained to autofluorecent cells is reported. 

For Ecocosm I, microscopy counts were not taken on experiment days 3, 4, and 14: some associated data sets under this project were sampled on these days. In order to provide cell counts on these additional sampling days, the abundances were interpolated for the missing days using a model 1 linear regression of the surrounding timepoints to calculate the "missing" days. These dates are denoted by "micoscopy_interpolated" under the "Method" parameter.

For the submitted file Results_BacAbundance_microscopy2.csv, the file was modified using the BCO-DMO laminar tool described below.

Converted the location information from the submission tool to decimal degrees to use with laminar to add latitude and longitude columns to the dataset.

Latitude: 34°55'32.42" N -> 34.925672
Longitude: 76°21'54.25" W -> -76.365069

Kept 6 decimal points precision from conversation with submitter.

With laminar pipeline:

Loaded the most recent dataset, which was attached to an email on 4/29/2024, named Results_Microbial_Abundance_microscopy_v2.csv.

Skipped loading row 2 which is a line with the following note
Note: -7777=below detection, -8888=NA, -9999=missing

Renamed headers to match BCO-DMO naming convention. Replaced periods and colons with underscores. Removed units from Excitation header because it will be noted in the Osprey parameters section. Removde µm from Size.Fraction because it will be noted in the Osprey parameters section. Replaced / with _per_. For BCO-DMO naming scheme, parameter names must start with a letter and not a number, so renamed the column name 620:tot to ratio_620_tot.

In the columns cells_per_L_avg and cells_per_L_sd, replaced the values (9,999) with -9999, replaced the values (7,777) with -7777 and replaced the values (7,777,000) with -7777 to match the flag format noted in the excel dataset submitted.

Removed commas from the numbers in columns cells_per_L_avg and cells_per_L_sd in order to convert the column into a number format.

Added latitude (lat) and longitude (lon) columns with static values from converted lat and lon submitted location information using 6 digit precision.

Converted the date format from %Y%m%d into an ISO date format of %Y-%m-%d.

Reordered the columns so that the metadata lat, lon, and Date columns are at the front of the table, right after the column Experiment_Name.

Saved the updated dataset to a file named 925050_v1_microbial_cellular_abundance.csv.

NSF Award Abstract:
The formation and flux of organic material is the foundation of ocean ecosystems, which in turn, substantially influences the global carbon cycle. As such, a fundamental goal in the ocean sciences is increasing our ability to identify marine organic matter's sources, transformations, and sinks, as well as how these components may change due to anthropogenic activities. Understanding these components is especially important in estuarine and coastal systems given these ecosystems are critical zones of organic carbon transformations. However, the dissolved organic carbon (DOC) pool in these systems consists of numerous different compounds from a multitude of sources that can turn over at vastly different rates (minutes to millennia). This makes it difficult to identify which DOC compounds support microbial growth, limiting the incorporation of microbial metabolism into predictive ecosystem models. Novel approaches are therefore needed to identify the DOC substrates driving microbial metabolism in ocean ecosystems. This project is premised on the idea that the bacterial cellular system is the ultimate chemical sensor of the organic environment and that the information recorded in the cell's active gene pool (transcripts) can be leveraged to make insights into DOC composition when the relationships between organic substrate availability, gene activity, and metabolism are known. This project identifies substrate-transcript relationships for a model marine bacterium, as well as the growth and metabolic outcomes of substrate availability. These insights are used to identify the biologically active DOC substrates in coastal environments when the model organism is added directly to coastal samples, and to interpret both historical and current environmental RNA and DNA data sets. This work provides novel insights into the substrates driving the ocean's carbon cycle and how marine bacterial cellular systems are regulated. Bioassays are developed that can be applied in many different aquatic environment settings. The project trains graduate and undergraduate students directly involved in the research and minority undergraduates will be recruited to use research modules for hands-on study of cell cultivation, bioinformatics, and microbial metabolism. High school students will be engaged through a module developed for an aquatic microbiology field trip and subsequent sample and data analysis.

Bacterial processing of dissolved organic carbon (DOC) mediates the flux of gigatons of carbon in the ocean, yet a significant hurdle to incorporating bacterial metabolism into ocean models is the inability to quantify the DOC substrates supporting bacterial metabolism and their transformation. Metatranscriptomics (sequencing of community mRNAs) has the potential to be a sensitive method for surveying bacterioplankton responses to the DOC pool and making insights into its composition but is currently limited by insufficient knowledge as to how transcriptional patterns relate to substrate availability. This project will identify carbon substrates supporting microbial metabolism and their transformation in estuarine-coastal ecosystems by elucidating the relationships between transcript abundances and carbon substrate availability. It aims to bridge the gap between model organism and environmental -omic studies by creating quantitative inventories of transcripts in response to defined substrates, and then using these calibrated transcriptional signals to interpret environmental DOC bioassays and metatranscriptomes. The first component of the project will establish genome-wide transcript-substrate relationships in a model marine bacterium in response to individual, environmentally-relevant carbon substrates. The second component will determine the extent to which transcription and metabolism are altered when the bacterium is exposed to complex mixtures of defined and undefined substrates, revealing the potential for transcription to identify individual substrates within a complex DOC pool and how metabolic processing may shape the DOC pools labile and refractory components. Finally, these calibrated transcriptional responses will be used to identify the DOC substrates driving bacterial metabolism in an estuarine-coastal system via DOC drawdown bioassays in which the model organism is added to natural seawater samples, as well as community wide bacterioplankton responses to the extant DOC pool via metatranscriptomics.