Inoculation Protocol

Inoculation Protocol#

Community Assembly#

Method#

  1. Navigate to shortcuts and click on ‘Bacteria Culture Workflow’ then ‘Inoculation via worklist’

  2. Select desired worklist-ensure it is ordered with media/water aliquots first (see multidispense best practices)

  3. Load input and destination lab ware (culture tubes, 96 well plate, etc)

  4. Begin the run

    • The Hamilton will run the water/media aliquots first, then aliquot the appropriate volume of cultures

Layout#

Inoculation Layout

Worklist Generation#

We developed an internal-use package to automate worklist generation in a consistent and reliable way for all kinds of experimental designs. The package is called worklistgen. (This is a work in progress, as we continue adding new features and fixing bugs. If you have any suggestions, please let us know.)

Usage Example#

# import worklistgen and other necessary libraries
import pandas as pd
from worklistgen.core import Source, Destination, Worklist

import random
from itertools import combinations

We want to set up everything for creating our worklist.

First, let’s define the species and media that we are going to use in our experiment.

# Define Species
species = ['EF', 'FP', 'BO', 'CD', 'DI', 'EL', 
           'BU', 'AC', 'GH', 'IJ', 'KL', 'MN', 
           'OP', 'QR', 'ST', 'UV', 'WX', 'YZ']
# Define Media
media = ['DM38', 'LB', 'M9', 'M9Glucose', 
         'M9Glycerol', 'M9Acetate', 'M9Propionate', 'M9Butyrate']

Now, we need to define our sources. We do this using the Source class. The Source class takes the following parameters:

  • name: The name (prefix) of the source. This is used to identify the source in the worklist.

  • source_type: The type of the source. This can be either "species" or "media".

  • labware_type: The type of labware used for the source. Different labware types have different predefined layouts.

  • input: A list of inputs.

The main attribute of the Source class is well_map, which contains the information about the location of each input in the labware.

Note that we have two sources: one for the species and one for the media. The species source contains the species we are going to be using in our experiment, and the media source contains the media conditions we are going to be using in our experiment. The species source is a 96-well plate, and the media source is a 24-tube trough.

# Define sources
source1 = Source(name='species_source', source_type='species', labware_type='96wellplate', input=species)
source2 = Source(name='media_source', source_type='media', labware_type='tuberack', input=media)

Lets check the well_map of our sources.

source1.well_map.head(5)
Source_type Input Well Name Labware_type
0 species EF A1 species_source-1 96wellplate
1 species FP B1 species_source-1 96wellplate
2 species BO C1 species_source-1 96wellplate
3 species CD D1 species_source-1 96wellplate
4 species DI E1 species_source-1 96wellplate 
source2.well_map.head(5)
Source_type Input Well Name Labware_type
0 media DM38 A1 media_source-1 tuberack
1 media LB A2 media_source-1 tuberack
2 media M9 A3 media_source-1 tuberack
3 media M9Glucose A4 media_source-1 tuberack
4 media M9Glycerol A5 media_source-1 tuberack

Now, we are going to define the location of each of the experimental conditions in the labware. We do this using the Destination class. The Destination class takes the following parameters:

  • name: The name (prefix) of the destination. This is used to identify the destination in the worklist.

  • labware_type: The type of labware used for the destination. Different labware types have different predefined layouts.

  • experiements: Data frame containing the experimental conditions. The first column should contain the species separated by “-” (community) and the second column should contain the media conditions separated by “-” (condition). The rest of the columns will be ignored.

  • blanks: List of blanks to include.

  • replicates: Number of replicates per experiemntal combination.

  • blanks_replicates: Number of replicates per blank.

Once again, the most importat attribute of the Destination class is well_map, which contains the information about the location of each input in the labware.

Here we are going to generate the experiemnts data frame randomly, but you can also use your own table. The only requirement is that the first column contains the species separated by “-” (community) and the second column contains the media conditions separated by “-” (condition). The rest of the columns will be ignored.

# Randomly generate communities
community_combinations = []
for size in range(1, len(species) + 1):
    community_combinations.extend(combinations(species, size))
random_communities = random.sample(community_combinations, 60)
communities = ['-'.join(community) for community in random_communities]

# Randomly generate conditions
condition_combinations = []
for size in range(1, len(media) + 1):
    condition_combinations.extend(combinations(media, size))
random_conditions = random.sample(condition_combinations, 60)
conditions = ['-'.join(condition) for condition in random_conditions]

# Build the experiments DataFrame
experiments = pd.DataFrame({
    'community': communities,
    'condition': conditions,
})
experiments.head(10)
community condition
0 FP-CD-DI-AC-GH-MN-QR-UV-WX DM38-M9-M9Glucose-M9Acetate-M9Butyrate
1 FP-BO-CD-DI-BU-AC-MN-OP-QR-ST-WX-YZ DM38-M9-M9Glucose-M9Glycerol-M9Acetate-M9Propi...
2 EF-FP-BO-EL-AC-KL-MN-OP-UV-YZ LB-M9-M9Glycerol
3 FP-BU-AC-MN-OP-UV M9-M9Glycerol-M9Acetate-M9Butyrate
4 EF-FP-BU-GH-KL-OP-QR-UV-WX M9-M9Glucose-M9Acetate
5 EF-FP-IJ-KL-MN-OP DM38-LB-M9-M9Glycerol
6 BO-CD-DI-BU-IJ-KL-MN-OP-QR-WX DM38-LB-M9-M9Propionate-M9Butyrate
7 FP-BO-DI-BU-AC-GH-IJ-MN-OP-ST-UV-YZ LB-M9Glucose-M9Propionate
8 EF-FP-DI-BU-AC-KL-MN-QR-UV M9Glycerol-M9Butyrate
9 CD-DI-BU-MN-ST-YZ DM38-M9Butyrate

Now, lets create or destination object and check its well_map.

# Define destination
destination = Destination(name='destination', labware_type='96wellplate', experiments=experiments, blanks=['M9Propionate', 'M9Butyrate'], replicates=3, blanks_replicates=2)
destination.well_map.head(10)
Community Condition Replicate Well Name Labware_type
0 blank M9Propionate 1 A1 destination-1 96wellplate
1 blank M9Propionate 2 B1 destination-1 96wellplate
2 blank M9Butyrate 1 C1 destination-1 96wellplate
3 blank M9Butyrate 2 D1 destination-1 96wellplate
4 FP-CD-DI-AC-GH-MN-QR-UV-WX DM38-M9-M9Glucose-M9Acetate-M9Butyrate 1 E1 destination-1 96wellplate
5 FP-CD-DI-AC-GH-MN-QR-UV-WX DM38-M9-M9Glucose-M9Acetate-M9Butyrate 2 F1 destination-1 96wellplate
6 FP-CD-DI-AC-GH-MN-QR-UV-WX DM38-M9-M9Glucose-M9Acetate-M9Butyrate 3 G1 destination-1 96wellplate
7 FP-BO-CD-DI-BU-AC-MN-OP-QR-ST-WX-YZ DM38-M9-M9Glucose-M9Glycerol-M9Acetate-M9Propi... 1 H1 destination-1 96wellplate
8 FP-BO-CD-DI-BU-AC-MN-OP-QR-ST-WX-YZ DM38-M9-M9Glucose-M9Glycerol-M9Acetate-M9Propi... 2 A2 destination-1 96wellplate
9 FP-BO-CD-DI-BU-AC-MN-OP-QR-ST-WX-YZ DM38-M9-M9Glucose-M9Glycerol-M9Acetate-M9Propi... 3 B2 destination-1 96wellplate

Finally, we create the Worklist object. The Worklist class takes the following parameters:

  • name: The name of the worklist.

  • total_volume: Total volume of each well.

Because we can have several sources and destinations, we need to add them to the worklist. We do this using the add_source() and add_destination() methods. After that, we can generate the worklist using the generate_worklist() method.

# Create worklist
worklist = Worklist(name='worklist', total_volume=100)
worklist.add_source(source1)
worklist.add_source(source2)
worklist.add_destination(destination)

# Generate worklist
worklist.generate_worklist()
worklist.worklist_df
Source_plate_Name Source_Plate_Type Source_Plate_Well Destination_plate_Name Destination_Plate_Type Destination_Plate_Well Transfer_Volume
0 media_source-1 tuberack A7 destination-1 96wellplate A1 100.000000
1 media_source-1 tuberack A7 destination-1 96wellplate B1 100.000000
2 media_source-1 tuberack A8 destination-1 96wellplate C1 100.000000
3 media_source-1 tuberack A8 destination-1 96wellplate D1 100.000000
4 species_source-1 96wellplate B1 destination-1 96wellplate E1 11.111111
... ... ... ... ... ... ... ...
2411 species_source-1 96wellplate F2 destination-2 96wellplate H11 14.285714
2412 species_source-1 96wellplate H2 destination-2 96wellplate H11 14.285714
2413 media_source-1 tuberack A1 destination-2 96wellplate H11 33.333333
2414 media_source-1 tuberack A2 destination-2 96wellplate H11 33.333333
2415 media_source-1 tuberack A3 destination-2 96wellplate H11 33.333333

2416 rows × 7 columns

Stamp#

Method#

  1. Load input and destination plates (specify locations with dialogue box)

  2. Navigate to shortcuts and click on ‘Bacteria Culture Workflow’ then ‘Inoculation/Sample Prep v2.3’

  3. Select Plate Stamp

  4. Select method parameters

    • Specify input and destination lab ware types and locations

    • Specify volume to transfer

    • Specify mix on source trough and number of cycles

  5. Begin the run

    • The Hamilton will stamp one by one from input(s) to destination plate and the run will finish

Layout#

Normalization#

Method#

  1. After SYBR DNA quantification excel sheet has been generated, run Hamilton_gDNA_normalization.py to generate worklist

  2. Navigate to shortcuts and click on ‘Bacteria Culture Workflow’ then ‘Inoculation via worklist’

  3. Load source DNA plate, water trough, and destination normalization plate

  4. Begin the run

    • The Hamilton will run the water aliquots first, then aliquot the appropriate volume of DNA to normalize it.

Layout#

Worklist Generation#

!python 250605_Hamilton_gDNA_normalization.py --excel gDNA_concentrations_calculation_matrix.xlsx --tag my_exp

gDNA worklist:

gDNA_worklist = pd.read_csv('my_exp_gDNA.csv')
gDNA_worklist.head(10)
Source_plate_Name Source_Plate_Type Source_Plate_Well Desination_plate_Name Destination_Plate_Type Destination_Well Transfer_Volume Tip_Size Liquid_Class
0 96raw RCK_CulturePlate_00 A1 96norm RCK_PCRPlate_00 A1 4 300 Water
1 96raw RCK_CulturePlate_00 B1 96norm RCK_PCRPlate_00 B1 16 300 Water
2 96raw RCK_CulturePlate_00 C1 96norm RCK_PCRPlate_00 C1 16 300 Water
3 96raw RCK_CulturePlate_00 D1 96norm RCK_PCRPlate_00 D1 31 300 Water
4 96raw RCK_CulturePlate_00 E1 96norm RCK_PCRPlate_00 E1 15 300 Water
5 96raw RCK_CulturePlate_00 F1 96norm RCK_PCRPlate_00 F1 19 300 Water
6 96raw RCK_CulturePlate_00 G1 96norm RCK_PCRPlate_00 G1 50 300 Water
7 96raw RCK_CulturePlate_00 H1 96norm RCK_PCRPlate_00 H1 50 300 Water
8 96raw RCK_CulturePlate_00 A2 96norm RCK_PCRPlate_00 A2 3 300 Water
9 96raw RCK_CulturePlate_00 B2 96norm RCK_PCRPlate_00 B2 16 300 Water

Water worklist:

gDNA_worklist = pd.read_csv('my_exp_water.csv')
gDNA_worklist.head(10)
Source_plate_Name Source_Plate_Type Source_Plate_Well Desination_plate_Name Destination_Plate_Type Destination_Well Transfer_Volume Tip_Size Liquid_Class
0 Water Source_plate_type A1 96norm RCK_PCRPlate_00 A1 96 300 Water
1 Water Source_plate_type B1 96norm RCK_PCRPlate_00 B1 34 300 Water
2 Water Source_plate_type C1 96norm RCK_PCRPlate_00 C1 34 300 Water
3 Water Source_plate_type D1 96norm RCK_PCRPlate_00 D1 19 300 Water
4 Water Source_plate_type E1 96norm RCK_PCRPlate_00 E1 35 300 Water
5 Water Source_plate_type F1 96norm RCK_PCRPlate_00 F1 31 300 Water
6 Water Source_plate_type G1 96norm RCK_PCRPlate_00 G1 0 300 Water
7 Water Source_plate_type H1 96norm RCK_PCRPlate_00 H1 0 300 Water
8 Water Source_plate_type A2 96norm RCK_PCRPlate_00 A2 47 300 Water
9 Water Source_plate_type B2 96norm RCK_PCRPlate_00 B2 34 300 Water
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