April 25

Lab 14: Poster Presentation and Abstract Submission

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April 18, 2019

Noah Mendoza

 

Purpose:

The purpose of this weeks lab was to revise our poster using Dr. Adair’s comments, record our metadata, and complete our abstracts.

 

Procedures:

  1. Cast vote on the CILICURE logo (A)
  2. Put soil metadata into the excel spreadsheet
  3. Relabel soil bag because we had positive PCR results
  4. Worked on poster edits and completed abstract

Data:

Metadata:

Group Members Section Group Soil-ID GPS Location Tree Species BHD (cm) pH Soil Texture Extraction Method
Rithvik Baratam, Noah Mendoza, Erick Cornejo 21 8 BCM21_8Sp19 -97.1188, 31.5485 Ilex vomitoria 67.94 7 Silty Loam Silica Bead

 

DNA Concentration/μl ∼volume μl PCR Soil Label on Bag
1115.6 500 + BCM21_8Sp19

Abstract:

Microbial soil diversity is vital to key processes that maintain soil health, through processes such as biogeochemical cycles, energy flow cycles, and food webs. In order to understand these interactions between microbial communities, the identification of unicellular Eukaryotas such as ciliates is crucial. The study aims to test a novel, cost-effective protocol that extracts  DNA from the soil using a silica bead method. The experiment concerned extraction, isolation, amplification, and analysis of environmental DNA. This form of non-isolated DNA was extracted from the soil in the rhizosphere of an Ilex vomitoria.  In this method of DNA extraction, the soil is ground with silica beads, charcoal, and DNA extraction buffer using a mortar and pestle to lyse encysted ciliates. This environmental DNA was then purified. PCR was used to amplify V4 18S rDNA region of pDNA (purified DNA). The test was positive for the presence of pDNA and it was determined that the pDNA had a  concentration of 1115.6 ng/µl.  The purpose of this study was to propose a new protocol that would serve as a favorable extraction method. The proposed silica-bead method was employed as a cost-effective alternative to lyse encysted ciliates and extract DNA without harming or compromising DNA integrity. Through the use of next-generation sequencing amplified DNA can be analyzed to determine the species and the amount present in the soil sample. The silica-bead method is an easy way to test the soil for the presence of microorganisms using PCR to amplify the DNA with primers. The sequencing of the eDNA will shed light on the microbial diversity found in the rhizosphere of the tree where the sample was collected.

Poster:

Poster Title: Sequencing of Ciliate DNA in Pursuit of Finding Diversity

Conclusion:

Since our poster and abstract have been completed, they are ready to be presented at CURES.

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February 22

Lab 6: Gel Electrophoresis and DNA Analysis

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February 21, 2019

Noah Mendoza

 

Purpose:

This week’s lab was centered around conducting gel electrophoresis and analyzing the results of our DNA using a nanodrop spectrophotometer and observing our agarose gels under UV light. These steps will be a part of the development of our protocol, jokingly known as the “BU method,” for studying ciliate diversity.

Materials:

  1. 10x loading buffer
  2. 1x TAE buffer
  3. 31ng DNA mass
  4. 125ng DNA mass
  5. 500ng DNA mass
  6. Agarose Gel
  7. Electrophoresis Tray
  8. Nanodrop spectrophotometer
  9. UV Transilluminator

Procedure:

DNA Sample:

Due to accidental error in storage of our sample, it had dried up and needed to be hydrated with sterile water.

  1. Take 50 μl of sterilized water and add it to the purified DNA sample.
  2. Pipette the sterile water and DNA solution repeatedly until thoroughly mixed.

Gel Electrophoresis:

  1. Remove the agarose gel that was prepared during the previous lab from the fridge.
  2. Transfer the agarose gel to the loading dock and cover the surface of the gel with 1x TAE buffer.
    1. Wear proper PPE while handling the agarose gel.
  3. Select the wells that will be used and decide what will be inserted into each well before adding anything into the wells.
  4. Using a micropipette, add 5 μl of 31 ng mass standard to the 5th well.
  5. Using a micropipette, add 5 μl of 125 ng mass standard to the 7th well.
  6. Using a micropipette, add 5 μl of 500 ng mass standard to the 8th well.
  7. Extract 9 μl of DNA and transfer it into an Eppendorf tube.
  8. Add 1 μl of the 10x loading buffer and mix with the DNA.
  9. Transfer the 10 μl mixture of DNA and buffer into the 6th well.
  10. Run the agarose gel for 20 minutes at 100 volts.
  11. Take the gel to the UV transilluminator and wait for the picture to be produced.
  12. Observe the results and record your observations.

DNA Concentration/Nanodrop Spectrophotometer

  1. Clean the nanodrop spectrophotometer and be sure that it is completely dry from any previous use
  2. Place 1 μl of DNA sample onto the surface of the nanodrop spectrophotometer
  3. Close the try and wait for the analysis
  4. Take a picture and record observations/results of your DNA sample

Observations:

Results:

Our DNA was concentrated at 1115.6 ng/μl with a 1.47/1.80 A260/A280 ratio and 0.89/2 for A260/A230. This shows us that our DNA was not completely pure and that there were impurities in our sample.

Storage:

All materials used were put back into their original locations. This includes the samples prepared from previous weeks. The desks were wiped clean and all disposable materials were thrown away.

Conclusion:

Using gel electrophoresis, we were able to prove that there was DNA in our sample. The agarose gel showed us the different sizes of our DNA bands compared to our mass standards, and we were able to analyze them under a UV transilluminator. Additional analysis will be conducted during future labs on our band sizes. We were also able to use a nanodrop spectrophotometer to gather data relating to the concentration of DNA present in our sample. Using this we found that out DNA contained impurities, and this is likely due to contamination or the introduction of sterilized water that was contaminated between leaving its container and being added to our sample.

Future Steps:

In the future we will conduct a PCR on the DNA that was found during our gel electrophoresis and nanodrop experiments. We will also be able to further compare our DNA bands to the bands of our mass standards.

 

 

 

 

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October 12

Lab 8: Data Analysis 10/11/18

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Objective:

The objective of this lab is to effectively use Excel to preform statistical analysis on the data we collected from our cell count and behavioral assay experiments. Using statistical analysis, we will be able to construct histograms, preform descriptive analysis, T-tests, and F-tests.

Procedure:

  1. Download the TookPak Analysis add in for Excel
  2. Organize your data into 4 columns, the first two for your cell counts and the other two for your behavioral assay.

Preforming Descriptive Statistics: Cell Counts for Control

  1. Click on the Data Tab and select “Data Analysis”
  2. Under Analysis Tools, select Descriptive Statistics.
  3. Select your input range, the Cell Counts for Control column
  4. Check the boxes for “summary statistics” and “confidence level for mean”
  5. Press “OK”

Preforming Descriptive Statistics: Cell Counts for Treatment

  1. Click on the Data Tab and select “Data Analysis”
  2. Under Analysis Tools, select Descriptive Statistics.
  3. Select your input range, as the Cell Counts for Treatments Column
  4. Check the boxes for “summary statistics” and “confidence level for mean”
  5. Press “OK”

Preforming Descriptive Statistics: Control Swim Speed

  1. Click on the Data Tab and select “Data Analysis”
  2. Under Analysis Tools, select Descriptive Statistics.
  3. Select your input range, as the Control Swim Speed Column
  4. Check the boxes for “summary statistics” and “confidence level for mean”
  5. Press “OK”

Preforming Descriptive Statistics: Treatment Swim Speed

  1. Click on the Data Tab and select “Data Analysis”
  2. Under Analysis Tools, select Descriptive Statistics.
  3. Select your input range, as the Treatment Swim Speed Column
  4. Check the boxes for “summary statistics” and “confidence level for mean”
  5. Press “OK”

Creating a Histogram: Cell Count Control

  1. Observe your data and see where most of your data points are.
  2. I selected to have a Bin in increments of 2000, and my values were from 2000 to 40,000.
  3. Click on the Data Tab and select “Data Analysis”
  4. Under Analysis Tools, select Histogram
  5. For the input range, select the Cell Count Control Column
  6. For the Bin Range, select the range that you set. (2000-40000 in increments of 2000)
  7. Select “New Worksheet Ply” and “Chart Output”

Creating a Histogram: Cell Count Treatment

  1. Observe your data and see where most of your data points are.
  2. I selected to have a Bin in increments of 5000, and my values were from 5000 to 150,000.
  3. Click on the Data Tab and select “Data Analysis”
  4. Under Analysis Tools, select Histogram
  5. For the input range, select the Cell Count Treatment Column
  6. For the Bin Range, select the range that you set. (5000-150,000 in increments of 5000)
  7. Select “New Worksheet Ply” and “Chart Output”
  8. Select “OK”

Creating a Histogram: Control Swim Speed

  1. Observe your data and see where most of your data points are.
  2. I selected to have a Bin in increments of 0.05, and my values were from 0.00 to 1.
  3. Click on the Data Tab and select “Data Analysis”
  4. Under Analysis Tools, select Histogram
  5. For the input range, select the Control Swim Speed Column
  6. For the Bin Range, select the range that you set. (0.00-1 in increments of 0.05)
  7. Select “New Worksheet Ply” and “Chart Output”
  8. Select “OK”

Creating a Histogram: Treatment Swim Speed

  1. Observe your data and see where most of your data points are.
  2. I selected to have a Bin in increments of 0.05, and my values were from 0.00 to 1.
  3. Click on the Data Tab and select “Data Analysis”
  4. Under Analysis Tools, select Histogram
  5. For the input range, select the Treatment Swim Speed Column
  6. For the Bin Range, select the range that you set. (0.00-1 in increments of 0.05)
  7. Select “New Worksheet Ply” and “Chart Output”
  8. Select “OK”

F-Test Two Samples for Cell Counts Procedure:

  1. Click the Data Tab and Select Data Analysis
  2. Select F-test: two samples
  3. For “Variable 1 Range” Select the Cell Count Control Column
  4. For “Variable 2 Range” Select the Cell Count Treatment Column
  5. Chose a random output range and press “OK”

T-Test Two Sample Assuming Unequal Variances for Cell Count Procedure

  1. Click the Data Tab and Select Data Analysis
  2. Select T-test: two sample assuming unequal variances
  3. For “Variable 1 Range” Select the Cell Count Control Column
  4. For “Variable 2 Range” Select the Cell Count Treatment Column
  5. Chose a random output range and press “OK”

F-Test Two Samples for Cell Swim Speed Procedure:

  1. Click the Data Tab and Select Data Analysis
  2. Select F-test: two samples
  3. For “Variable 1 Range” Select the Cell Swim Speed Control Column
  4. For “Variable 2 Range” Select the Cell Swim Speed Treatment Column
  5. Chose a random output range and press “OK”

T-Test Two Sample Assuming Unequal Variances for Cell Swim Speed Procedure:

  1. Click the Data Tab and Select Data Analysis
  2. Select T-test: two sample assuming unequal variances
  3. For “Variable 1 Range” Select the Cell Swim Speed Control Column
  4. For “Variable 2 Range” Select the Cell Swim Speed Treatment Column
  5. Chose a random output range and press “OK”

Data and Analysis:

 

Cell Counts for Control
Mean 19067.61111
Standard Error 2778.754145
Median 17375
Mode 2300
Standard Deviation 16672.52487
Sample Variance 277973085.4
Kurtosis 2.206329842
Skewness 1.48173658
Range 68800
Minimum 1200
Maximum 70000
Sum 686434
Count 36
Confidence Level (95.0%) 5641.170819

 

 

Cell Counts for Treatment
Mean 46153.88889
Standard Error 11362.74492
Median 21997.5
Mode 16000
Standard Deviation 68176.46955
Sample Variance 4648031000
Kurtosis 6.571781986
Skewness 2.721715213
Range 265500
Minimum 1500
Maximum 267000
Sum 1661540
Count 36
Confidence Level (95.0%) 23067.59856

 

Control Swim Speed mm/s
Mean 0.39982
Standard Error 0.016672247
Median 0.39
Mode 0.5
Standard Deviation 0.117890591
Sample Variance 0.013898191
Kurtosis -0.871219591
Skewness -0.001181841
Range 0.454
Minimum 0.2
Maximum 0.654
Sum 19.991
Count 50
Confidence Level (95.0%) 0.033504135

 

Treatment Swim Speed mm/s
Mean 0.40543425
Standard Error 0.020604526
Median 0.402
Mode 0.33
Standard Deviation 0.130314467
Sample Variance 0.01698186
Kurtosis 1.291242912
Skewness 0.679440683
Range 0.6657
Minimum 0.1603
Maximum 0.826
Sum 16.21737
Count 40
Confidence Level (95.0%) 0.041676588

 

 

F-Test Two-Sample for Ciliate Counts
  Control Treatment
Mean 19067.61111 46153.88889
Variance 277973085.4 4648031000
Observations 36 36
df 35 35
F 0.059804482
P(F<=f) one-tail 1.32006E-13
F Critical one-tail 0.56910677

 

t-Test: Two-Sample Assuming Unequal Variances for Cell Count
  Control Treatment
Mean 19067.61111 46153.88889
Variance 277973085.4 4648031000
Observations 36 36
Hypothesized Mean Difference 0
df 39
t Stat -2.315544835
P(T<=t) one-tail 0.01296619
t Critical one-tail 1.684875122
P(T<=t) two-tail 0.02593238
t Critical two-tail 2.02269092

 

F-Test Two-Sample for Variances
  Control Treatment
Mean 0.39982 0.40543425
Variance 0.013898191 0.01698186
Observations 50 40
df 49 39
F 0.818413958
P(F<=f) one-tail 0.251421026
F Critical one-tail 0.608734983

 

t-Test: Two-Sample Assuming Unequal Variances
  Variable 1 Variable 2
Mean 0.39982 0.40543425
Variance 0.013898191 0.01698186
Observations 50 40
Hypothesized Mean Difference 0
df 80
t Stat -0.211819232
P(T<=t) one-tail 0.416393391
t Critical one-tail 1.664124579
P(T<=t) two-tail 0.832786783
t Critical two-tail 1.990063421

Storage:

All computers used during lab time were left in their original condition for the next lab. The excel spreadsheet was saved for future use.

Conclusion:

This lab aided me in becoming proficient in the statistical analyses conducted during this lab. Being able to read the data from the descriptive statistics helped me to gauge where by BIN range should be and at what intervals I should include in the range. This lab made me comfortable preforming T-tests and F-tests using the excel program. During the T and F tests, we had to reject the null hypothesis due to the p-value not having any significance.

Future Goals:

I would like to learn about preforming different statistical analyses using the Excel program, because I find it difficult to navigate if you don’t have very clear instructions on how to preform the analysis needed. Stemming from this, I will play around with excel to learn how to do other analyses with my data. I would also like to become faster at transcribing data from one spreadsheet to another, even with the use of then special paste tool.

 

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October 5

Lab 7: Cell Count and Behavioral Assay 10/4/18

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Objective:

The objectives for this lab were to practice conduction cell assays and to practice cell counting techniques. These objectives were meet by comparing the stick culture of Tetrahymena to the treatment culture with the polypropylene.

Procedure:

Ciliate Count:

  1.  Using a serological pipette, aseptically transfer 5ml of the treatment and the control, from the 50ml flask, into separate sterile glass tubes. Be sure to swirl the flask before pipetting and to pipette from the top of the flask.
  2.  Once you have moved your 5ml of cultures, be sure to label the treatment and the control and to cover them with caps.
  3. Using a pipette, put 3 drops of 2μl of each culture onto 2 separate slides.
  4. Add 1μl of iodine to each of the drops of culture.
  5. Using your phone or the camera provided, take a picture to count with accuracy.

Swim Speed Assay:

  1. Place a 20μl drop of culture onto a flat slide.
  2. Place a metric ruler under the slide and adjust the slide so that you can view under a dissection microscope. Be sure you’re able to see the mm markings under the microscope.
  3. Pick a cell to watch, and time how quickly it swims 1mm.
  4. Repeat this and record the times for 10 different cells.
  5. Using your data, calculate the average time and standard deviation.

Optical Density:

  1. Zero the spectrophotometer
  2. Life the lid and place your test tube into the spectrophotometer.
  3. Record the optical density of the black solution first.
  4. Record the optical density of the Control second.
  5. Record the optical density of the Treatment third.

Data and Analysis:

Control Group:

Trials Cell Count Cell Concentration (cell/ml)
1 30 1.5×10^4
2 45 2.25×10^4
3 40 2.0×10^4
Average 38.3 cells 1.91×10^4

Treatment Group:

Trials Cell Count Cell Concentration (cells/ml)
1 45 cells 2.25×10^4
2 50 cells 2.5×10^4
3 30cells 1.5×10^4
Average 41.6 cells 2.08×10^4

Swim Speed Assay:

Trial Time (Seconds) Average Speed (mm/s)
1 3 0.33 mm/s
2 4 0.25 mm/s
3 2 0.5 mm/s
4 3 0.33 mm/s
5 4 0.25 mm/s
6 3 0.33 mm/s
7 2 0.5 mm/s
8 4 0.25 mm/s
9 3 0.33 mm/s
10 2 0.5 mm/s
Average 3 seconds 0.357 mm/s
Standard Deviation 0.81 –

Optical Density:

PPT 0.00
PPT + Twine Juice (treated media) 0.066
PPT + TH (Media +Tetrahymena) 0.001
PPT+TJ + TH 0.065

 

Storage:

All materials used were returned to their original locations after the completion of the lab. Slides were washed with a bleach solution, thoroughly cleaned, and left to dry on paper towels. The microscopes were covered and returned to the middle of the tables. Any pipettor tips used were discarded and the pipettors were returned to their holders. Iodine, the treatment, and the control were left on the table for future labs.

Future Goals:

In the future, I would like to be able to test other types of microplastics on Tetrahymena. Even being able to test the different types of twine used in the different labs would be interesting. I would also like to test how different stains/chemicals affect the different assays we have tested in class. Personally, I would like to get more comfortable conducting the other assays, instead of doing the same one continuously.

Conclusion:

This lab provided me with more practice doing dilutions, conducting cell counts, and conducting speed assays. To complete this lab, I also had to interact with the microscopes and make wet mounts. I feel much more comfortable making wet mounts and observing cultures through microscopes. I enjoyed the new experience of using the spectrophotometer and the serological pipette to measure my cultures.

 

 

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