1 of 11

Renã A. S. Robinson, Ph.D.

Professor of Chemistry

Vanderbilt University

Writing Workshop

(Overview, Experimental, Results)

2 of 11

2

General Writing Advice

Getting a submission ready manuscript takes longer than you think

plan accordingly

3 of 11

3

  • Envision the manuscript at the onset of the study

  • Perform a thorough literature review on your subject prior to writing

  • Read your groups relevant papers, read field specific papers, re-read many times

  • Gain clarity before writing

  • Write frequently

  • Seek writing center or other resources for English language rules

  • Recognize technical writing is different than other writing styles

  • Writing is an iterative process

General Writing Advice

4 of 11

4

RASR Team Writing Process

Literature review & Endnote begins

Study Design & Goals

Title & Authors & Journal

Collect Data

Review & analyze data

Draft Figures & Tables

Review & Edit

Draft Exptl’

Draft Results

Draft Intro

Draft Disc’

Draft Abstract

Submit first full draft to RASR

Review & edit

Share with Co-authors

Review & edit

Prepare submission

Submit

Wait

Respond to Reviewer's

Wait

Acceptance

Galley proofs

Publication

Citation

Update CV

5 of 11

5

Start with prior template from group

6 of 11

6

Drafting the Experimental Section

Protein Depletion

SCCS sample batches (N=58) were depleted using one of two Waters e2695 HPLC system with a multiple affinity removal human 14 (MARS-14, Agilent) depletion column and buffer system. Each batch was divided into two sequences consisting of seven and nine duplicate injections for daytime and overnight runs, respectively. Three QC1 samples were diluted with 120 µL Buffer A and depleted at the start of a new MARS-14 column and one depleted with every sequence. Unbound peak retention times of QC1 were used to track instrument performance and determined the need of instrument maintenance between batches. Unbound fractions were collected, concentrated using 10 kDa centrifugal filters (Amicon) and quantified by bicinchoninic acid (BCA) protein assays. Addition of BCA reagents were performed on the Biomek. Depleted samples were stored at –80 °C until ready for further analysis.

From Nekesa Oliver, manuscript in progress

7 of 11

7

Drafting the Experimental Section

Protein Depletion

SCCS sample batches (N=58) were depleted using one of two Waters e2695 HPLC system with a multiple affinity removal human 14 (MARS-14, Agilent) depletion column and buffer system. Each batch was divided into two sequences consisting of seven and nine duplicate injections for daytime and overnight runs, respectively. Three QC1 samples (??) were diluted with 120 µL Buffer A and depleted at the start of a new MARS-14 column and one depleted with every sequence. Unbound peak retention times of QC1 were used to track instrument performance and determined the need of instrument maintenance between batches. Unbound fractions were collected ???, concentrated using 10 kDa centrifugal filters (Amicon) and quantified by bicinchoninic acid (BCA) protein assays. Addition of BCA reagents were performed on the Biomek. Depleted samples were stored at –80 °C until ready for further analysis.

8 of 11

8

Writing up a Result for a Figure

Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses

  • Khiry L. Patterson,1 Albert B. Arul,1 Min Ji Choi,1 Nekesa Oliver,1 Marsalas D. Whitaker,1 Angel C. Bodrick,2 Logan Dumitrescu,4 Julia O’Malley,4 Shania Hansen,5 Angela L. Jefferson,4,6 Timothy J. Hohman,5-6 and Renã A. S. Robinson1, 3-6*

9 of 11

9

Writing up a Result for a Figure

2.0

1.5

1.0

0.5

0

-0.5

-1.0

-1.5

Peptide count

A.

-15

-10

-5

0

5

QC 212

QC 31

QC 139

QC 213

QC 259

QC 262

QC 258

QC 260

QC 260

QC 160/161

QC 159

B.

 

-15

-10

-5

0

5

QC 212

QC 31

QC 213

QC 262

QC 29

QC 41

5.5

4.0

2.5

1.0

-0.5

-2.0

-3.5

-5.0

5

3

1

-1

-3

-5

-7

-9

PSMs

C.

-3

-1.5

0

1.5

3

QC 212

QC 31

QC 213

QC 262

QC 223

QC 224

QC 225

5.5

3.5

1.5

-0.5

-2.5

-4.5

-6.5

-8.5

D.

% m/z

-8

-4

0

4

8

QC 212

QC 31

QC 213

QC 262

QC 142

QC 144

QC 143

QC 226

QC 227

QC 222

4

3

2

1

0

-1

-2

-3

MS/MS spectra

E.

-5

0

5

10

15

QC 212

QC 31

QC 213

QC 262

QC 144

QC 212

QC 31

QC 213

QC 262

QC 261

QC 259

QC 257

QC 142

QC 265

QC 266

QC 264

QC 260

QC 267

QC 270

QC 268

QC 269

QC 258

3

1.5

0

-1.5

-3.0

-4.5

-6.0

-7.5

F.

% ITMAX

-3

-1

1

3

5

QC 143

Protein count

Protein count

 

10 of 11

10

Writing up a Result for a Figure

2.0

1.5

1.0

0.5

0

-0.5

-1.0

-1.5

Peptide count

A.

-15

-10

-5

0

5

QC 212

QC 31

QC 139

QC 213

QC 259

QC 262

QC 258

QC 260

QC 260

QC 160/161

QC 159

B.

 

-15

-10

-5

0

5

QC 212

QC 31

QC 213

QC 262

QC 29

QC 41

5.5

4.0

2.5

1.0

-0.5

-2.0

-3.5

-5.0

5

3

1

-1

-3

-5

-7

-9

PSMs

C.

-3

-1.5

0

1.5

3

QC 212

QC 31

QC 213

QC 262

QC 223

QC 224

QC 225

5.5

3.5

1.5

-0.5

-2.5

-4.5

-6.5

-8.5

D.

% m/z

-8

-4

0

4

8

QC 212

QC 31

QC 213

QC 262

QC 142

QC 144

QC 143

QC 226

QC 227

QC 222

4

3

2

1

0

-1

-2

-3

MS/MS spectra

E.

-5

0

5

10

15

QC 212

QC 31

QC 213

QC 262

QC 144

QC 212

QC 31

QC 213

QC 262

QC 261

QC 259

QC 257

QC 142

QC 265

QC 266

QC 264

QC 260

QC 267

QC 270

QC 268

QC 269

QC 258

3

1.5

0

-1.5

-3.0

-4.5

-6.0

-7.5

F.

% ITMAX

-3

-1

1

3

5

QC 143

Protein count

Protein count

Principle component analysis (PCA) was used to assess how consistent and reproducible quality control samples were on the basis of six key instrumental metrics: XXXXXXXXXX.

Generally, QC samples (N = XXX) clustered into a single large group based on peptide abundance, % m/z, MS/MS spectra and %IT max. For metrics such as peptide count and PSMs, QCs clustered into two distinguishable groups ( Figure 4A and C). For each metric, there were also outliers which fell outside of 95% of the QC samples within the entire group. For example, on the basis of peptide count, 10 QC samples were outliers (Figure 4A, labeled in orange) and these same outliers were also present for other metrics such as XXXXXXXXXX. Fewer outlilers were observed for traditional QC metrics such as peptide abundance, PSMs and MS/MS spectra, however more informative metrics such as %m/z and IT max had greater QC outliers.

….

11 of 11

11

General Writing Advice

Once we have words on the paper, we have something to work with & you can get feedback to help you move forward.